DeRainMamba：一种用于图像去雨的频率感知状态空间模型与细节增强
DeRainMamba: A Frequency-Aware State Space Model with Detail Enhancement for Image Deraining
AI总结：
提出DeRainMamba，通过频率感知状态空间模块与多方向感知卷积，在单图像去雨中兼顾去雨与细节保留，以更少参数和计算成本在四个基准上超越现有方法。
摘要：
图像去雨对于提升视觉质量和支持可靠的下游视觉任务至关重要。尽管基于Mamba的模型提供了高效的序列建模，但其捕捉细粒度细节的能力有限且缺乏频域感知，这限制了进一步的性能提升。为解决这些问题，我们提出了DeRainMamba，它集成了频率感知状态空间模块（FASSM）和多方向感知卷积（MDPConv）。FASSM利用傅里叶变换区分雨条纹与高频图像细节，在去雨与细节保留之间取得平衡。MDPConv通过捕捉各向异性梯度特征并高效融合多个卷积分支，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指标上持续优于最先进的方法，同时需要更少的参数和更低的计算成本。这些结果验证了在状态空间框架内结合频域建模与空间细节增强用于单图像去雨的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
