Single Image LDR to HDR Conversion using Conditional Diffusion (2307.02814v1)
Abstract: Digital imaging aims to replicate realistic scenes, but Low Dynamic Range (LDR) cameras cannot represent the wide dynamic range of real scenes, resulting in under-/overexposed images. This paper presents a deep learning-based approach for recovering intricate details from shadows and highlights while reconstructing High Dynamic Range (HDR) images. We formulate the problem as an image-to-image (I2I) translation task and propose a conditional Denoising Diffusion Probabilistic Model (DDPM) based framework using classifier-free guidance. We incorporate a deep CNN-based autoencoder in our proposed framework to enhance the quality of the latent representation of the input LDR image used for conditioning. Moreover, we introduce a new loss function for LDR-HDR translation tasks, termed Exposure Loss. This loss helps direct gradients in the opposite direction of the saturation, further improving the results' quality. By conducting comprehensive quantitative and qualitative experiments, we have effectively demonstrated the proficiency of our proposed method. The results indicate that a simple conditional diffusion-based method can replace the complex camera pipeline-based architectures.
- “Single-image hdr reconstruction by learning to reverse the camera pipeline,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 1651–1660.
- “Hdr-cgan: single ldr to hdr image translation using conditional gan,” in Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing, 2021, pp. 1–9.
- “Hdr image reconstruction from a single exposure using deep cnns,” ACM transactions on graphics (TOG), vol. 36, no. 6, pp. 1–15, 2017.
- “Deep reverse tone mapping.,” ACM Trans. Graph., vol. 36, no. 6, pp. 177–1, 2017.
- “Recovering high dynamic range radiance maps from photographs,” in ACM SIGGRAPH 2008 classes, pp. 1–10. 2008.
- “Classifier-free diffusion guidance,” arXiv preprint arXiv:2207.12598, 2022.
- “simple diffusion: End-to-end diffusion for high resolution images,” arXiv preprint arXiv:2301.11093, 2023.
- “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 586–595.
- “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851, 2020.
- “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, 2015, pp. 234–241.
- “Diffusion models beat gans on image synthesis,” Advances in Neural Information Processing Systems, vol. 34, pp. 8780–8794, 2021.
- “Imagen video: High definition video generation with diffusion models,” arXiv preprint arXiv:2210.02303, 2022.
- “Improved denoising diffusion probabilistic models,” in International Conference on Machine Learning. PMLR, 2021, pp. 8162–8171.
- “Denoising diffusion implicit models,” arXiv preprint arXiv:2010.02502, 2020.
- “Diffusion autoencoders: Toward a meaningful and decodable representation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10619–10629.
- “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” arXiv preprint arXiv:2112.10741, 2021.
- “Expandnet: A deep convolutional neural network for high dynamic range expansion from low dynamic range content,” in Computer Graphics Forum. Wiley Online Library, 2018, vol. 37, pp. 37–49.
- “Extracting and composing robust features with denoising autoencoders,” in Proceedings of the 25th international conference on Machine learning, 2008, pp. 1096–1103.
- “Hdr-vdp-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions,” ACM Transactions on graphics (TOG), vol. 30, no. 4, pp. 1–14, 2011.
- “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing, vol. 13, no. 4, pp. 600–612, 2004.