用频谱结构引导去雨，提升图像恢复效率与精度。
Spectral-Structured Diffusion for Single-Image Rain Removal
- 在频域引入结构化噪声扰动，逐步抑制多方向雨线
- 相比现有方法，模型更紧凑且推理更快
- 适合需要高效去雨的实时场景应用
雨痕表现为方向性明确、频谱集中的多尺度重叠结构，给单图去雨带来挑战。尽管基于扩散模型的修复框架具备渐进去噪能力，但传统的空间域扩散未显式建模此类频谱特性。本文提出SpectralDiff，一种面向单图去雨的频谱结构化扩散框架。不改变扩散公式，而是通过引入结构化频谱扰动，引导逐步抑制多方向雨线成分。为此，设计全乘积U-Net架构，利用卷积定理将卷积操作替换为逐元素乘法层，在保持建模能力的同时显著提升计算效率。在合成与真实世界基准上的大量实验表明，SpectralDiff在去雨性能上达到竞争力水平，同时模型更轻量、推理更高效，优于现有扩散方法。
原文摘要 · Abstract (English)
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.
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