Reproduce “DiffIR: Efficient Diffusion Model for Image Restoration” on OpenResearch
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About this paper
Bin Xia, Yulun Zhang, Shiyin Wang, Yitong Wang, Xinglong Wu, Yapeng Tian, Wenming Yang, Luc Van Gool
DiffIR introduces an efficient Diffusion Model for image restoration that applies the diffusion process to a compact prior representation rather than full images or large latent spaces. This approach achieves state-of-the-art results in tasks like inpainting, super-resolution, and deblurring, with significantly reduced computational costs compared to prior diffusion models.
Key insights
- Diffusing a compact prior representation is vastly more efficient for image restoration than diffusing full images or large latent spaces, enabling state-of-the-art performance with significantly fewer iterations.
- Jointly optimizing the diffusion model and the restoration network, by performing all (few) diffusion steps during training, enhances robustness against IPR estimation errors and leads to optimal restoration.
- Dynamically modulating a transformer architecture with a learned compact prior effectively guides local feature aggregation and global interaction modeling for precise image reconstruction.
Summary by alphaXiv.
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