Title: Diff-SSR: Diffusion Model with Structure-Modulated for Image Super-Resolution
Primary area: generative models
Keywords: super resolution, diffusion

Abstract: Recent advances in diffusion models, like Stable Diffusion, have been shown to significantly improve performance in image super-resolution (SR) tasks. However, existing diffusion techniques often sample noise from just one distribution, which limits their effectiveness when dealing with complex scenes or intricate textures in different semantic areas. With the advent of the segment anything model (SAM), it has become possible to create highly detailed region masks that can improve the recovery of fine details in diffusion SR models. Despite this, incorporating SAM directly into SR models significantly increases computational demands. In this paper, we propose the Diff-SSR model, which can utilize the fine-grained structure information from SAM in the process of sampling noise to improve the image quality without additional computational cost during inference. In the process of training, we encode structural position information into the segmentation mask from SAM. Then the encoded mask is integrated into the forward diffusion process by modulating it to the sampled noise. This adjustment allows us to independently adapt the noise mean within each corresponding segmentation area. The diffusion model is trained to estimate this modulated noise. Crucially, our proposed framework does NOT change the reverse diffusion process and does NOT require SAM at inference. Experimental results demonstrate the effectiveness of our proposed method, which exhibits the fewest artifacts compared to other generated models.