SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution

TL;DR

SAM-DiffSR enhances diffusion-based image super-resolution by integrating structural information from SAM during training, improving detail recovery and artifact suppression without increasing inference computational cost.

Contribution

The paper introduces a novel method that encodes structural information from SAM into the diffusion process, boosting super-resolution quality without extra inference costs.

Findings

Outperforms existing diffusion-based SR methods by 0.74 dB PSNR on DIV2K.

Effectively suppresses artifacts in super-resolved images.

Does not require SAM during inference, reducing computational load.

Abstract

Diffusion-based super-resolution (SR) models have recently garnered significant attention due to their potent restoration capabilities. But conventional diffusion models perform noise sampling from a single distribution, constraining their ability to handle real-world scenes and complex textures across semantic regions. With the success of segment anything model (SAM), generating sufficiently fine-grained region masks can enhance the detail recovery of diffusion-based SR model. However, directly integrating SAM into SR models will result in much higher computational cost. In this paper, we propose the SAM-DiffSR 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, showcasing superior performance in suppressing artifacts, and surpassing existing diffusion-based methods by 0.74 dB at the maximum in terms of PSNR on DIV2K dataset. The code and dataset are available at https://github.com/lose4578/SAM-DiffSR.