Diffusion Posterior Proximal Sampling for Image Restoration

• URL: http://arxiv.org/abs/2402.16907v1
• Date: Sun, 25 Feb 2024 04:24:28 GMT
• Title: Diffusion Posterior Proximal Sampling for Image Restoration
• Authors: Hongjie Wu, Linchao He, Mingqin Zhang, Dongdong Chen, Kunming Luo, Mengting Luo, Ji-Zhe Zhou, Hu Chen, Jiancheng Lv
• Abstract summary: Diffusion-based image restoration algorithms exploit pre-trained diffusion models to leverage data priors. These strategies initiate the denoising process with pure white noise and incorporate random noise at each generative step, leading to over-smoothed results. In this paper, we introduce a refined paradigm for diffusion-based image restoration.
• Score: 28.388405376136095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have demonstrated remarkable efficacy in generating high-quality samples. Existing diffusion-based image restoration algorithms exploit pre-trained diffusion models to leverage data priors, yet they still preserve elements inherited from the unconditional generation paradigm. These strategies initiate the denoising process with pure white noise and incorporate random noise at each generative step, leading to over-smoothed results. In this paper, we introduce a refined paradigm for diffusion-based image restoration. Specifically, we opt for a sample consistent with the measurement identity at each generative step, exploiting the sampling selection as an avenue for output stability and enhancement. Besides, we start the restoration process with an initialization combined with the measurement signal, providing supplementary information to better align the generative process. Extensive experimental results and analyses validate the effectiveness of our proposed approach across diverse image restoration tasks.

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