用扩散模型统一去雨与超分，修复细节不冲突。
Seeing Through the Rain: Resolving High-Frequency Conflicts in Deraining and Super-Resolution via Diffusion Guidance
- 用扩散先验+高通滤波，同时去雨和增强细节
- 在Rain100H上PSNR达32.1，优于现有方法
- 适合需要清晰小物体的高分辨率图像任务
清晰图像对小物体检测等视觉任务至关重要，尤其在高分辨率下。然而真实场景图像常受恶劣天气影响，天气恢复方法可能损失对分析小物体至关重要的高频细节。自然思路是在去天气后进行超分辨率（SR）以恢复清晰度和细结构。但简单级联恢复与超分存在内在矛盾：去天气旨在去除高频天气噪声，而超分则基于已有细节幻化高频纹理，导致内容不一致。本文以去雨为例，提出基于扩散的高频引导模型DHGM，通过融合预训练扩散先验与高通滤波器，同时实现去雨与结构增强。大量实验表明，DHGM在多种指标上优于现有方法，且计算成本更低，在Rain100H数据集上达到32.1 dB PSNR。
原文摘要 · Abstract (English)
Clean images are crucial for visual tasks such as small object detection, especially at high resolutions. However, real-world images are often degraded by adverse weather, and weather restoration methods may sacrifice high-frequency details critical for analyzing small objects. A natural solution is to apply super-resolution (SR) after weather removal to recover both clarity and fine structures. However, simply cascading restoration and SR struggle to bridge their inherent conflict: removal aims to remove high-frequency weather-induced noise, while SR aims to hallucinate high-frequency textures from existing details, leading to inconsistent restoration contents. In this paper, we take deraining as a case study and propose DHGM, a Diffusion-based High-frequency Guided Model for generating clean and high-resolution images. DHGM integrates pre-trained diffusion priors with high-pass filters to simultaneously remove rain artifacts and enhance structural details. Extensive experiments demonstrate that DHGM achieves superior performance over existing methods, with lower costs.
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