针对复杂真实退化图像，提出结构保持的扩散模型修复方法
Degradation-Aware and Structure-Preserving Diffusion for Real-World Image Super-Resolution
- 注入退化统计信息，让模型显式感知图像退化类型
- 根据边缘强度调节噪声，更好保护图像结构细节
- 轻量级模块，可无缝集成到现有扩散模型中
真实世界图像超分辨率对扩散模型而言极具挑战，因实际退化复杂且异质，通常未被显式建模。本文提出一种退化感知且结构保持的扩散框架。提出退化感知标记注入，从低分辨率输入中编码轻量级退化统计，并与语义条件特征融合，实现显式的退化感知修复。进一步提出空间非对称噪声注入，通过局部边缘强度调节扩散噪声，提升训练中对结构区域的保护。两个模块均为轻量级附加组件，仅需对条件管道进行微小修改。在DIV2K和RealSR数据集上的实验表明，该方法在无参考感知质量上表现优异，视觉修复结果更真实，同时保持良好的感知-失真权衡。消融实验证明各模块有效，组合后具有互补增益。代码与模型已公开于https://github.com/jiyang0315/DASP-SR.git。
原文摘要 · Abstract (English)
Real-world image super-resolution is particularly challenging for diffusion models because real degradations are complex, heterogeneous, and rarely modeled explicitly. We propose a degradation-aware and structure-preserving diffusion framework for real-world SR. Specifically, we introduce Degradation-aware Token Injection, which encodes lightweight degradation statistics from low-resolution inputs and fuses them with semantic conditioning features, enabling explicit degradation-aware restoration. We further propose Spatially Asymmetric Noise Injection, which modulates diffusion noise with local edge strength to better preserve structural regions during training. Both modules are lightweight add-ons to the adopted diffusion SR framework, requiring only minor modifications to the conditioning pipeline. Experiments on DIV2K and RealSR show that our method delivers competitive no-reference perceptual quality and visually more realistic restoration results than recent baselines, while maintaining a favorable perception--distortion trade-off. Ablations confirm the effectiveness of each module and their complementary gains when combined. The code and model are publicly available at https://github.com/jiyang0315/DASP-SR.git.
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