Unsupervised Diffusion-Based Degradation Modeling for Real-World Super-Resolution
Yuying Chen, Mingde Yao, Wenbo Li, Renjing Pei, Jinjing Zhao, Wenqi Ren
Abstract
Single image super-solution (SR) aims to restore a high-resolution (HR) image from a degraded low-resolution (LR) image. However, existing SR models still face a significant domain gap between synthetic and real-world datasets due to the mismatched degradation distributions, hindering SR models from achieving optimal results. In this paper, we propose an unsupervised diffusion-based degradation modeling framework (UDDM) to effectively capture real-world degradation distributions. Specifically, given unpaired LR and HR images, a diffusion-based degradation module (DDM) first models the degradation distribution by diffusing real-world LR images to downsampled LR images, which does not require HR images. It then applies reverse diffusion to generate real-world LR images from extremely downsampled HR images. This approach allows DDM to model and generate real-world degradation distributions without requiring paired data, by using extreme downsampling to link unpaired LR and HR images. Additionally, we introduce a physics-based dynamic degradation module (P-DDM) that adaptively models content-aware degradation, ensuring both content and structural accuracy. Finally, the LR images generated by DDM and P-DDM are adaptively weighted to produce the final LR images, which are paired with the given HR images for training the SR network. Extensive experiments across multiple real-world datasets demonstrate that our framework achieves state-of-the-art performance in both qualitative and quantitative comparison.
BibTeX
@article{Chen_Yao_Li_Pei_Zhao_Ren_2025, title={Unsupervised Diffusion-Based Degradation Modeling for Real-World Super-Resolution}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/32235}, DOI={10.1609/aaai.v39i2.32235}, abstractNote={Single image super-solution (SR) aims to restore a high-resolution (HR) image from a degraded low-resolution (LR) image. However, existing SR models still face a significant domain gap between synthetic and real-world datasets due to the mismatched degradation distributions, hindering SR models from achieving optimal results. In this paper, we propose an unsupervised diffusion-based degradation modeling framework (UDDM) to effectively capture real-world degradation distributions. Specifically, given unpaired LR and HR images, a diffusion-based degradation module (DDM) first models the degradation distribution by diffusing real-world LR images to downsampled LR images, which does not require HR images. It then applies reverse diffusion to generate real-world LR images from extremely downsampled HR images. This approach allows DDM to model and generate real-world degradation distributions without requiring paired data, by using extreme downsampling to link unpaired LR and HR images. Additionally, we introduce a physics-based dynamic degradation module (P-DDM) that adaptively models content-aware degradation, ensuring both content and structural accuracy. Finally, the LR images generated by DDM and P-DDM are adaptively weighted to produce the final LR images, which are paired with the given HR images for training the SR network. Extensive experiments across multiple real-world datasets demonstrate that our framework achieves state-of-the-art performance in both qualitative and quantitative comparison.}, number={2}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chen, Yuying and Yao, Mingde and Li, Wenbo and Pei, Renjing and Zhao, Jinjing and Ren, Wenqi}, year={2025}, month={Apr.}, pages={2348-2356} }