通过频域感知与细节增强，提升图像去雨的清晰度与效率
DeRainMamba: A Frequency-Aware State Space Model with Detail Enhancement for Image Deraining
- 引入频域感知模块，区分雨痕与细节高频信息
- 在四个数据集上实现更高PSNR与SSIM，参数更少、计算更低
- 适合需要高效高保真去雨的视觉任务应用
图像去雨对提升视觉质量及支持下游视觉任务至关重要。尽管基于Mamba的模型具备高效的序列建模能力，但其在捕捉细粒度细节和缺乏频域感知方面仍存在局限。为此，我们提出DeRainMamba，融合频域感知状态空间模块（FASSM）与多方向感知卷积（MDPConv）。FASSM利用傅里叶变换区分雨痕与高频图像细节，在去除雨痕的同时保持细节。MDPConv通过捕获各向异性梯度特征并高效融合多分支卷积，进一步恢复局部结构。在四个公开基准上的大量实验表明，DeRainMamba在PSNR和SSIM上持续优于当前最优方法，同时参数量更少、计算开销更低。结果验证了在状态空间框架中结合频域建模与空间细节增强的有效性，适用于单图像去雨任务。
原文摘要 · Abstract (English)
Image deraining is crucial for improving visual quality and supporting reliable downstream vision tasks. Although Mamba-based models provide efficient sequence modeling, their limited ability to capture fine-grained details and lack of frequency-domain awareness restrict further improvements. To address these issues, we propose DeRainMamba, which integrates a Frequency-Aware State-Space Module (FASSM) and Multi-Directional Perception Convolution (MDPConv). FASSM leverages Fourier transform to distinguish rain streaks from high-frequency image details, balancing rain removal and detail preservation. MDPConv further restores local structures by capturing anisotropic gradient features and efficiently fusing multiple convolution branches. Extensive experiments on four public benchmarks demonstrate that DeRainMamba consistently outperforms state-of-the-art methods in PSNR and SSIM, while requiring fewer parameters and lower computational costs. These results validate the effectiveness of combining frequency-domain modeling and spatial detail enhancement within a state-space framework for single image deraining.
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