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

TL;DR

CDFormer introduces a diffusion-based, content-aware transformer approach for blind image super-resolution, effectively capturing degradation and content details to outperform existing methods and set new benchmarks.

Contribution

The paper proposes a novel diffusion-based module to learn Content Degradation Prior and an adaptive SR network, advancing blind super-resolution techniques.

Findings

Outperforms existing blind SR methods on benchmarks

Establishes new state-of-the-art performance

Effectively models content and degradation information

Abstract

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 \href{https://github.com/I2-Multimedia-Lab/CDFormer}{https://github.com/I2-Multimedia-Lab/CDFormer}.