  Diffusion models, known for their powerful generative capabilities, play a
crucial role in addressing real-world super-resolution challenges. However,
these models often focus on improving local textures while neglecting the
impacts of global degradation, which can significantly reduce semantic fidelity
and lead to inaccurate reconstructions and suboptimal super-resolution
performance. To address this issue, we introduce a novel two-stage,
degradation-aware framework that enhances the diffusion model's ability to
recognize content and degradation in low-resolution images. In the first stage,
we employ unsupervised contrastive learning to obtain representations of image
degradations. In the second stage, we integrate a degradation-aware module into
a simplified ControlNet, enabling flexible adaptation to various degradations
based on the learned representations. Furthermore, we decompose the
degradation-aware features into global semantics and local details branches,
which are then injected into the diffusion denoising module to modulate the
target generation. Our method effectively recovers semantically precise and
photorealistic details, particularly under significant degradation conditions,
demonstrating state-of-the-art performance across various benchmarks. Codes
will be released at https://github.com/bichunyang419/DeeDSR.
