  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.
