DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
(2308.15070)Abstract
We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework. DiffBIR decouples blind image restoration problem into two stages: 1) degradation removal: removing image-independent content; 2) information regeneration: generating the lost image content. Each stage is developed independently but they work seamlessly in a cascaded manner. In the first stage, we use restoration modules to remove degradations and obtain high-fidelity restored results. For the second stage, we propose IRControlNet that leverages the generative ability of latent diffusion models to generate realistic details. Specifically, IRControlNet is trained based on specially produced condition images without distracting noisy content for stable generation performance. Moreover, we design a region-adaptive restoration guidance that can modify the denoising process during inference without model re-training, allowing users to balance realness and fidelity through a tunable guidance scale. Extensive experiments have demonstrated DiffBIR's superiority over state-of-the-art approaches for blind image super-resolution, blind face restoration and blind image denoising tasks on both synthetic and real-world datasets. The code is available at https://github.com/XPixelGroup/DiffBIR.
Overview
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DiffBIR introduces a two-stage pipeline combining a restoration module and a generative diffusion model to address blind image restoration, dealing with diverse and unknown degradations.
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The methodology employs a comprehensive degradation model to replicate real-world conditions and leverages pretrained Stable Diffusion models fine-tuned for restoration tasks.
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Experimental validation across multiple datasets shows DiffBIR's superior performance in restoring high-fidelity and realistic details, outperforming existing state-of-the-art methods.
An Examination of DiffBIR: Advancements in Blind Image Restoration via Generative Diffusion Models
The paper "DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior" presents a sophisticated framework called DiffBIR, aiming to tackle the challenges of blind image restoration (BIR) by leveraging pretrained text-to-image diffusion models. The authors propose a method that incorporates a generative diffusion prior to enhance the restoration quality of images experiencing diverse and unknown degradations typically encountered in real-world scenarios.
Methodological Overview
Two-Stage Pipeline
DiffBIR operates on a two-stage pipeline:
- Restoration Module (RM):
- Pretrained to handle a variety of degradations using a modified version of SwinIR.
- Removes significant noise and artifacts, producing an image termed $I_{reg}$.
- A regression loss ($\mathcal{L}2$) is optimized during training to minimize the difference between the high-quality image ($I{HQ}$) and $I_{reg}$.
- Generative Module:
- Utilizes pretrained Stable Diffusion models, finetuned with an injective modulation sub-network called LAControlNet.
- Aims to generate realistic textures and details to replace what was lost during the degradation process.
- Involves training the Stable Diffusion with $I_{reg}$ as conditioning, maintaining its generative prowess while tailoring it to image restoration.
Degradation Model and Loss Functions
The degradation model employed mirrors real-world conditions by encompassing diverse and high-order degradations such as Gaussian and Poisson noise, JPEG compression artifacts, and various resizing methods. This comprehensive degradation model is used to synthesize low-quality (LQ) images for training.
The latent diffusion process involves conditioning on the latent space representation of $I{reg}$ while optimizing a latent diffusion loss, blending the generative and restorative capabilities. Additionally, a controllable module is introduced to balance between the sharp generative output ($I{diff}$) and smoother, more artifact-free results by modifying the gradient scale during the denoising procedure.
Experimental Validation
Datasets and Metrics
The authors extensively validate DiffBIR across various datasets:
- Synthetic Datasets: CelebA-Test for faces, a collection of images processed to emulate real-world degradations.
- Real-World Datasets: RealSRSet, LFW-Test, CelebChild-Test, and WIDER-Test, among others.
The evaluations use conventional metrics such as PSNR, SSIM, LPIPS, and FID for quantitative assessments, while MANIQA and NIQE are applied to evaluate no-reference image quality. Identity preservation metrics (IDS) are used specifically for BFR tasks.
Results and Comparisons
DiffBIR demonstrates superior performance in most metrics across diverse tasks:
- BSR Tasks: On RealSRSet and Real47 datasets, DiffBIR outperforms state-of-the-art methods such as Real-ESRGAN+ and BSRGAN in MANIQA scores.
- BFR Tasks: On datasets like CelebA-Test and LFW-Test, DiffBIR achieves leading scores in FID and competitive results in other metrics like PSNR and IDS.
Qualitatively, DiffBIR's results exhibit significant improvements in detail reconstruction and artifact removal, outperforming methods like Real-ESRGAN+ and VQFR in both semantic regions and complex texture reconstruction.
Implications and Future Directions
Practical Implications
The introduction of the two-stage pipeline in DiffBIR presents an effective strategy for addressing BIR problems in practical applications. The generative capabilities of the latent diffusion models, combined with robust degradation removal from the restoration module, mark a notable advancement in restoring high-fidelity and realistic details.
Theoretical Implications
DiffBIR's method of integrating diffusion models with image restoration tasks opens avenues for further research in exploring and exploiting the capabilities of diffusion models beyond traditional synthesis and into more applied domains such as image enhancement and inpainting.
Future Work
Looking forward, the potential integration of text-driven guidance in the restoration process suggests a compelling direction for exploring how semantic information can be richly incorporated into restoration tasks. Additionally, improving the efficiency of the inference process is paramount, as current computational demands of DiffBIR require significant resources which may limit its accessibility for broader applications.
Conclusion
DiffBIR offers a substantial contribution to the field of blind image restoration, harmonizing the strengths of diffusion models and traditional restoration techniques. Through detailed experimental evaluations and robust methodological advancements, DiffBIR sets a new standard for achieving high-quality image restoration in both synthetic and real-world scenarios.
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