Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolution

arXiv:2404.10688v1 Announce Type: cross
Abstract: Image super-resolution is a fundamentally ill-posed problem because multiple valid high-resolution images exist for one low-resolution image. Super-resolution methods based on diffusion probabilistic models can deal with the ill-posed nature by learning the distribution of high-resolution images conditioned on low-resolution images, avoiding the problem of blurry images in PSNR-oriented methods. However, existing diffusion-based super-resolution methods have high time consumption with the use of iterative sampling, while the quality and consistency of generated images are less …

arxiv cs.cv cs.lg diffusion diffusion model flow image probability resolution sampling type