DeRainMamba: A Frequency-Aware State Space Model with Detail Enhancement for Image Deraining
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912634898481152 |
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| author | Zhu, Zhiliang Zeng, Tao Yang, Tao Luo, Guoliang Zeng, Jiyong |
| author_facet | Zhu, Zhiliang Zeng, Tao Yang, Tao Luo, Guoliang Zeng, Jiyong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06746 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeRainMamba: A Frequency-Aware State Space Model with Detail Enhancement for Image Deraining Zhu, Zhiliang Zeng, Tao Yang, Tao Luo, Guoliang Zeng, Jiyong Computer Vision and Pattern Recognition 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. |
| title | DeRainMamba: A Frequency-Aware State Space Model with Detail Enhancement for Image Deraining |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.06746 |