CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution

• URL: http://arxiv.org/abs/2302.12831v1
• Date: Tue, 14 Feb 2023 15:13:33 GMT
• Title: CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
• Authors: Axi Niu, Kang Zhang, Trung X. Pham, Jinqiu Sun, Yu Zhu, In So Kweon, Yanning Zhang
• Abstract summary: Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation. We propose a simple but non-trivial DPM-based super-resolution post-process framework,i.e., cDPMSR. Our method surpasses prior attempts on both qualitative and quantitative results.
• Score: 91.56337748920662
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resolution (SR) have shown that iteratively refining a pure Gaussian noise with a conditional image using a U-Net trained on denoising at various-level noises can help obtain a satisfied high-resolution image for the low-resolution one. To further improve the performance and simplify current DPM-based super-resolution methods, we propose a simple but non-trivial DPM-based super-resolution post-process framework,i.e., cDPMSR. After applying a pre-trained SR model on the to-be-test LR image to provide the conditional input, we adapt the standard DPM to conduct conditional image generation and perform super-resolution through a deterministic iterative denoising process. Our method surpasses prior attempts on both qualitative and quantitative results and can generate more photo-realistic counterparts for the low-resolution images with various benchmark datasets including Set5, Set14, Urban100, BSD100, and Manga109. Code will be published after accepted.

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