Deblur, super-resolution, and inpainting using an efficient diffusion model with DiffPIR
Deblur, super-resolution, and inpainting using an efficient diffusion model with DiffPIR
Denoising Diffusion Models for Plug-and-Play Image Restoration
arXiv paper abstract https://arxiv.org/abs/2305.08995
arXiv PDF paper https://arxiv.org/pdf/2305.08995.pdf
Plug-and-play Image Restoration (IR) ... a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser as the implicit image prior.
... diffusion models ... potential to serve as a generative denoiser prior to the plug-and-play IR methods remains to be further explored.
... other ... diffusion models for image restoration ... fail to achieve satisfactory results or ... require an unacceptable number of Neural Function Evaluations (NFEs) during inference.
This paper proposes DiffPIR, which integrates the traditional plug-and-play method into the diffusion sampling framework.
Compared to plug-and-play IR methods that rely on discriminative Gaussian denoisers, DiffPIR is expected to inherit the generative ability of diffusion models.
Experimental ... on three ... IR tasks, including super-resolution, image deblurring, and inpainting, demonstrate ... DiffPIR achieves state-of-the-art performance ... with no more than 100 NFEs ...
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