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ViStripformer: A Token-Efficient Transformer for Versatile Video Restoration (2312.14502v1)
Published 22 Dec 2023 in cs.CV
Abstract: Video restoration is a low-level vision task that seeks to restore clean, sharp videos from quality-degraded frames. One would use the temporal information from adjacent frames to make video restoration successful. Recently, the success of the Transformer has raised awareness in the computer-vision community. However, its self-attention mechanism requires much memory, which is unsuitable for high-resolution vision tasks like video restoration. In this paper, we propose ViStripformer (Video Stripformer), which utilizes spatio-temporal strip attention to catch long-range data correlations, consisting of intra-frame strip attention (Intra-SA) and inter-frame strip attention (Inter-SA) for extracting spatial and temporal information. It decomposes video frames into strip-shaped features in horizontal and vertical directions for Intra-SA and Inter-SA to address degradation patterns with various orientations and magnitudes. Besides, ViStripformer is an effective and efficient transformer architecture with much lower memory usage than the vanilla transformer. Extensive experiments show that the proposed model achieves superior results with fast inference time on video restoration tasks, including video deblurring, demoireing, and deraining.
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[2018] Liu, J., Yang, W., Yang, S., Guo, Z.: Erase or fill? deep joint recurrent rain removal and reconstruction in videos. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3233–3242 (2018) Su et al. [2017] Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich, W., Wang, O.: Deep video deblurring for hand-held cameras. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1279–1288 (2017) Sajjadi et al. [2018] Sajjadi, M.S.M., Vemulapalli, R., Brown, M.: Frame-recurrent video super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6626–6634 (2018) Zamir et al. [2021] Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H., Shao, L.: Multi-stage progressive image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 14816–14826 (2021) Wang et al. [2022] Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: A general u-shaped transformer for image restoration. In: Proc. Conf. Comput. Vis. 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[2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H.: Restormer: Efficient transformer for high-resolution image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5718–5729 (2022) Cho et al. [2021] Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., Ko, S.-J.: Rethinking coarse-to-fine approach in single image deblurring. In: Proc. Int. Conf. Comput. Vis., pp. 4641–4650 (2021) Li et al. [2022] Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 17431–17441 (2022) Chen et al. [2021] Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 12299–12310 (2021) Tsai et al. [2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. 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[2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. 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[2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. 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[2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. 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[2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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[2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. 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[2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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[2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. 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[2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. 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[2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. 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[2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. 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Pattern Recognit., pp. 8102–8111 (2019) Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. 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[2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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[2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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Pattern Recognit., pp. 8102–8111 (2019) Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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[2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H.: Restormer: Efficient transformer for high-resolution image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5718–5729 (2022) Cho et al. [2021] Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., Ko, S.-J.: Rethinking coarse-to-fine approach in single image deblurring. In: Proc. Int. Conf. Comput. Vis., pp. 4641–4650 (2021) Li et al. [2022] Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 17431–17441 (2022) Chen et al. [2021] Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 12299–12310 (2021) Tsai et al. [2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. 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[2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. 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In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. 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[2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. 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Pattern Recognit., pp. 8102–8111 (2019) Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. 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[2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. 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Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. 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Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. 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[2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. 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[2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. 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Pattern Recognit., pp. 8102–8111 (2019) Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. 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Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2021] Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., Ko, S.-J.: Rethinking coarse-to-fine approach in single image deblurring. In: Proc. Int. Conf. Comput. Vis., pp. 4641–4650 (2021) Li et al. [2022] Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 17431–17441 (2022) Chen et al. [2021] Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 12299–12310 (2021) Tsai et al. [2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. 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[2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Liu, J., Yang, W., Yang, S., Guo, Z.: Erase or fill? deep joint recurrent rain removal and reconstruction in videos. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3233–3242 (2018) Su et al. [2017] Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich, W., Wang, O.: Deep video deblurring for hand-held cameras. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1279–1288 (2017) Sajjadi et al. [2018] Sajjadi, M.S.M., Vemulapalli, R., Brown, M.: Frame-recurrent video super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6626–6634 (2018) Zamir et al. [2021] Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H., Shao, L.: Multi-stage progressive image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 14816–14826 (2021) Wang et al. [2022] Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: A general u-shaped transformer for image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 17662–17672 (2022) Zamir et al. [2022] Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H.: Restormer: Efficient transformer for high-resolution image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5718–5729 (2022) Cho et al. [2021] Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., Ko, S.-J.: Rethinking coarse-to-fine approach in single image deblurring. In: Proc. Int. Conf. Comput. Vis., pp. 4641–4650 (2021) Li et al. [2022] Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proc. Conf. Comput. Vis. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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Pattern Recognit., pp. 17431–17441 (2022) Chen et al. [2021] Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 12299–12310 (2021) Tsai et al. [2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2021] Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., Ko, S.-J.: Rethinking coarse-to-fine approach in single image deblurring. In: Proc. Int. Conf. Comput. Vis., pp. 4641–4650 (2021) Li et al. [2022] Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 17431–17441 (2022) Chen et al. [2021] Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 12299–12310 (2021) Tsai et al. [2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. 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[2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Liu, J., Yang, W., Yang, S., Guo, Z.: Erase or fill? deep joint recurrent rain removal and reconstruction in videos. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3233–3242 (2018) Su et al. [2017] Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich, W., Wang, O.: Deep video deblurring for hand-held cameras. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1279–1288 (2017) Sajjadi et al. [2018] Sajjadi, M.S.M., Vemulapalli, R., Brown, M.: Frame-recurrent video super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6626–6634 (2018) Zamir et al. [2021] Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H., Shao, L.: Multi-stage progressive image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 14816–14826 (2021) Wang et al. [2022] Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: A general u-shaped transformer for image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 17662–17672 (2022) Zamir et al. [2022] Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H.: Restormer: Efficient transformer for high-resolution image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5718–5729 (2022) Cho et al. [2021] Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., Ko, S.-J.: Rethinking coarse-to-fine approach in single image deblurring. In: Proc. Int. Conf. Comput. Vis., pp. 4641–4650 (2021) Li et al. [2022] Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proc. Conf. Comput. Vis. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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Pattern Recognit., pp. 17431–17441 (2022) Chen et al. [2021] Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 12299–12310 (2021) Tsai et al. [2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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Pattern Recognit., pp. 8102–8111 (2019) Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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[2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. 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Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H.: Restormer: Efficient transformer for high-resolution image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5718–5729 (2022) Cho et al. [2021] Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., Ko, S.-J.: Rethinking coarse-to-fine approach in single image deblurring. In: Proc. Int. Conf. Comput. Vis., pp. 4641–4650 (2021) Li et al. [2022] Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 17431–17441 (2022) Chen et al. [2021] Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 12299–12310 (2021) Tsai et al. [2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. 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[2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. 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[2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. 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[2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. 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Pattern Recognit., pp. 8102–8111 (2019) Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. 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Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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Pattern Recognit., pp. 5718–5729 (2022) Cho et al. [2021] Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., Ko, S.-J.: Rethinking coarse-to-fine approach in single image deblurring. In: Proc. Int. Conf. Comput. Vis., pp. 4641–4650 (2021) Li et al. [2022] Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 17431–17441 (2022) Chen et al. [2021] Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 12299–12310 (2021) Tsai et al. [2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. 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[2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. 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[2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. 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[2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. 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[2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. 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Pattern Recognit., pp. 8102–8111 (2019) Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. 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Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. 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[2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H.: Restormer: Efficient transformer for high-resolution image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5718–5729 (2022) Cho et al. [2021] Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., Ko, S.-J.: Rethinking coarse-to-fine approach in single image deblurring. In: Proc. Int. Conf. Comput. Vis., pp. 4641–4650 (2021) Li et al. [2022] Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 17431–17441 (2022) Chen et al. [2021] Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 12299–12310 (2021) Tsai et al. [2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. 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[2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H.: Restormer: Efficient transformer for high-resolution image restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5718–5729 (2022) Cho et al. [2021] Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., Ko, S.-J.: Rethinking coarse-to-fine approach in single image deblurring. In: Proc. Int. Conf. Comput. Vis., pp. 4641–4650 (2021) Li et al. [2022] Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 17431–17441 (2022) Chen et al. [2021] Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 12299–12310 (2021) Tsai et al. [2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. 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Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. 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Pattern Recognit., pp. 8102–8111 (2019) Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. 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[2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. 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[2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. 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[2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 12299–12310 (2021) Tsai et al. [2022] Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. 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[2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. 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[2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. 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[2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. 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[2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. 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[2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. 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[2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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[2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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[2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. 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[2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. 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Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. 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[2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., Lin, C.-W.: Stripformer: Strip transformer for fast image deblurring. In: Proc. European Conf. Computer Vis., pp. 146–162 (2022) Yang et al. [2020] Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5791–5800 (2020) Chi et al. [2021] Chi, Z., Wang, Y., Yu, Y., Tang, J.: Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9133–9142 (2021) Suin et al. [2020] Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. 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[2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. 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[2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. 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[2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. 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[2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Suin, M., Purohit, K., Rajagopalan, A.N.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3606–3615 (2020) Jiang et al. [2022] Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S.: Erdn: Equivalent receptive field deformable network for video deblurring. In: Proc. European Conf. Computer Vis., pp. 663–678 (2022) Patil et al. [2022] Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. 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Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. 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[2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Patil, P.W., Gupta, S., Rana, S., Venkatesh, S.: Video restoration framework and its meta-adaptations to data-poor conditions. In: Proc. European Conf. Computer Vis., pp. 143–160 (2022) Kim and Lee [2015] Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. 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Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Kim, T.H., Lee, K.M.: Generalized video deblurring for dynamic scenes. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 5426–5434 (2015) Zhou et al. [2022] Zhou, K., Li, W., Lu, L., Han, X., Lu, J.: Revisiting temporal alignment for video restoration. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 6043–6052 (2022) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. 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Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. 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Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. 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[2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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[2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Proc. Neural Inf. Process. Syst., pp. 6000–6010 (2017) Dosovitskiy et al. [2021] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. 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[2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. Learn. Repre. (2021) Liu et al. 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[2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proc. Int. Conf. Comput. Vis., pp. 9992–10002 (2021) Chu et al. [2021] Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. In: Proc. Neural Inf. Process. Syst., pp. 9355–9366 (2021) Chen et al. [2022] Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Chen, C.-F., Panda, R., Fan, Q.: Regionvit: Regional-to-local attention for vision transformers. In: Proc. Int. Conf. Learn. Repre. (2022) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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[2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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[2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. 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Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. 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[2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. European Conf. Computer Vis., pp. 213–229 (2020) Ranftl et al. [2021] Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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[2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. 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[2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. 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[2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. 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[2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. Int. Conf. Comput. Vis., pp. 12159–12168 (2021) Zhu et al. [2021] Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. 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[2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. 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Pattern Recognit., pp. 8102–8111 (2019) Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Repre. (2021) Xie et al. [2021] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. 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[2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Proc. Neural Inf. Process. Syst., pp. 12077–12090 (2021) Lin et al. [2022] Lin, J., Cai, Y., Hu, X., Wang, H., Yan, Y., Zou, X., Ding, H., Zhang, Y., Timofte, R., Van Gool, L.: Flow-guided sparse transformer for video deblurring. In: Proc. Int. Conf. Mach. Learn., pp. 13334–13343 (2022) Zeng et al. [2020] Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. 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[2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. [2018] Jiang, T.-X., Huang, T.-Z., Zhao, X.-L., Deng, L.-J., Wang, Y.: Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Trans. Image Process. (4), 2089–2102 (2018) Wang et al. [2022] Wang, S., Zhu, L., Fu, H., Qin, J., SchÖnlieb, C.-B., Feng, W., Wang, S.: Rethinking video rain streak removal: A new synthesis model and a deraining network with video rain prior. In: Proc. European Conf. Computer Vis., pp. 565–582 (2022) Sun et al. [2018] Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018) Zheng et al. [2020] Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3633–3642 (2020) He et al. [2019] He, B., Wang, C., Shi, B., Duan, L.-Y.: Mop moire patterns using mopnet. In: Proc. Int. Conf. Comput. Vis., pp. 2424–2432 (2019) He et al. [2020] He, B., Wang, C., Shi, B., Duan, L.-Y.: Fhde2net: Full high definition demoireing network. In: Proc. European Conf. Computer Vis., pp. 713–729 (2020) Liu et al. [2020] Liu, L., Liu, J., Yuan, S., Slabaugh, G., Leonardis, A., Zhou, W., Tian, Q.: Wavelet-based dual-branch network for image demoireing. In: Proc. European Conf. Computer Vis., pp. 86–102 (2020) Yu et al. [2022] Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoireing. In: Proc. European Conf. Computer Vis., pp. 646–662 (2022) Ji and Yao [2022] Ji, B., Yao, A.: Multi-scale memory-based video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 1909–1918 (2022) Lai et al. [2017] Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 624–632 (2017) Nah et al. [2017] Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 3883–3891 (2017) Nah et al. [2019] Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8102–8111 (2019) Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Proc. European Conf. Computer Vis., pp. 528–543 (2020) Wang et al. [2018] Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 7794–7803 (2018) Liang et al. [2022a] Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. In: arXiv Preprint arXiv:2201.12288 (2022) Liang et al. [2022b] Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. In: Proc. Neural Inf. Process. Syst., pp. 378–393 (2022) Sun et al. [2018] Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 8934–8943 (2018) Pan et al. [2023] Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 22191–22200 (2023) Li et al. [2023] Li, D., Shi, X., Zhang, Y., Cheung, K.C., See, S., Wang, X., Qin, H., Li, H.: A simple baseline for video restoration with grouped spatial-temporal shift. In: Proc. Conf. Comput. Vis. Pattern Recognit., pp. 9822–9832 (2023) Jiang et al. 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