Diffusion Posterior Proximal Sampling for Image Restoration
Hongjie Wu Linchao He Mingqin Zhang Dongdong Chen Kunming Luo Mengting Luo Ji-Zhe Zhou Hu Chen Jiancheng Lv
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.