Efficient Diffusion Model for Image Restoration by Residual Shifting (doi:10.21979/N9/VYPJ0O)

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Efficient Diffusion Model for Image Restoration by Residual Shifting

doi:10.21979/N9/VYPJ0O

DR-NTU (Data)

2024-10-04

1

Yue, Zongsheng; Wang, Jianyi; Loy, Chen Change, 2024, "Efficient Diffusion Model for Image Restoration by Residual Shifting", https://doi.org/10.21979/N9/VYPJ0O, DR-NTU (Data), V1

Efficient Diffusion Model for Image Restoration by Residual Shifting

doi:10.21979/N9/VYPJ0O

Yue, Zongsheng (Nanyang Technological University)

Wang, Jianyi (Nanyang Technological University)

Loy, Chen Change (Nanyang Technological University)

Python

DR-NTU (Data)

Yue, Zongsheng

Yue, Zongsheng

2024-10-03

https://doi.org/10.21979/N9/VYPJ0O

Computer and Information Science, Computer and Information Science, Image super-resolution, Diffusion model

While diffusion-based image restoration (IR) methods have achieved remarkable success, they are still limited by the low inference speed attributed to the necessity of executing hundreds or even thousands of sampling steps. Existing acceleration sampling techniques, though seeking to expedite the process, inevitably sacrifice performance to some extent, resulting in over-blurry restored outcomes. To address this issue, this study proposes a novel and efficient diffusion model for IR that significantly reduces the required number of diffusion steps. Our method avoids the need for post-acceleration during inference, thereby avoiding the associated performance deterioration. Specifically, our proposed method establishes a Markov chain that facilitates the transitions between the high-quality and low-quality images by shifting their residuals, substantially improving the transition efficiency. A carefully formulated noise schedule is devised to flexibly control the shifting speed and the noise strength during the diffusion process. Extensive experimental evaluations demonstrate that the proposed method achieves superior or comparable performance to current state-of-the-art methods on three classical IR tasks, namely image super-resolution, image inpainting, and blind face restoration, even only with four sampling steps.

Codes

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Github: <a href="https://github.com/zsyOAOA/ResShift">link</a>

10.48550/arXiv.2403.07319

Yue, Z., Wang, J., & Loy, C. C. (2024). Efficient diffusion model for image restoration by residual shifting. arXiv preprint arXiv:2403.07319.

10356/181036

Yue, Z., Wang, J. & Loy, C. C. (2024). Efficient diffusion model for image restoration by residual shifting. IEEE Transactions On Pattern Analysis and Machine Intelligence, 3461721-.