Degradation-Aware and Structure-Preserving Diffusion for Real-World Image Super-Resolution

• URL: http://arxiv.org/abs/2604.11470v1
• Date: Mon, 13 Apr 2026 13:43:22 GMT
• Title: Degradation-Aware and Structure-Preserving Diffusion for Real-World Image Super-Resolution
• Abstract summary: We propose a degradation-aware and structure-preserving diffusion framework for real-world SR.<n>Specifically, we introduce Degradation-aware Token Injection, which encodes lightweight degradation statistics from low-resolution inputs.<n>We also propose Spatially Asymmetric Noise Injection, which modulates diffusion noise with local edge strength to better preserve structural regions during training.
• Score: 3.663940075220745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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