CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution

Qingguo Liu, Chenyi Zhuang, Pan Gao, Jie Qin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 7455-7464

Existing Blind image Super-Resolution (BSR) methods focus on estimating either kernel or degradation information but have long overlooked the essential content details. In this paper we propose a novel BSR approach Content-aware Degradation-driven Transformer (CDFormer) to capture both degradation and content representations. However low-resolution images cannot provide enough content details and thus we introduce a diffusion-based module CDFormer_ diff to first learn Content Degradation Prior (CDP) in both low- and high-resolution images and then approximate the real distribution given only low-resolution information. Moreover we apply an adaptive SR network CDFormer_ SR that effectively utilizes CDP to refine features. Compared to previous diffusion-based SR methods we treat the diffusion model as an estimator that can overcome the limitations of expensive sampling time and excessive diversity. Experiments show that CDFormer can outperform existing methods establishing a new state-of-the-art performance on various benchmarks under blind settings. Codes and models will be available at https://github.com/I2-Multimedia-Lab/CDFormer.