Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09054 (cs)
[Submitted on 10 Mar 2026]
Title:Spectral-Structured Diffusion for Single-Image Rain Removal
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Abstract:Rain streaks manifest as directional and frequency-concentrated structures that overlap across multiple scales, making single-image rain removal particularly challenging. While diffusion-based restoration models provide a powerful framework for progressive denoising, standard spatial-domain diffusion does not explicitly account for such structured spectral characteristics. We introduce SpectralDiff, a spectral-structured diffusion-based framework tailored for single-image rain removal. Rather than redefining the diffusion formulation, our method incorporates structured spectral perturbations to guide the progressive suppression of multi-directional rain components. To support this design, we further propose a full-product U-Net architecture that leverages the convolution theorem to replace convolution operations with element-wise product layers, improving computational efficiency while preserving modeling capacity. Extensive experiments on synthetic and real-world benchmarks demonstrate that SpectralDiff achieves competitive rain removal performance with improved model compactness and favorable inference efficiency compared to existing diffusion-based approaches.
| Comments: | |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.09054 [cs.CV] |
| (or arXiv:2603.09054v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09054
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View a PDF of the paper titled Spectral-Structured Diffusion for Single-Image Rain Removal, by Yucheng Xing and 1 other authors
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