  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}.
