Abstract: Image deraining aims to restore high-quality clean images from rain-degraded inputs and is a key technique for improving the robustness of outdoor vision systems. Existing convolutional neural network based methods are limited by local receptive fields and thus struggle to effectively model long-range dependencies. Although some Transformer-based methods enhance global modeling capability, they still show limited ability in handling multi-scale and directional rain streaks as well as recovering local high-frequency details. Moreover, existing frequency-enhanced methods often treat the frequency domain merely as an auxiliary representation, lacking fine-grained collaborative modeling of the complementary relationship between the spatial and frequency domains. To address these issues, this paper proposes a synergizing frequency and spatial network (SynFSNet) for image deraining. Unlike existing methods that mainly use the frequency domain as a global enhancement tool, the proposed method starts from the directional, density-related, and scale-varying characteristics of rain streaks in the frequency domain. Specifically, a Multi-Scale Fourier Fusion Module (MSFFM) is designed to progressively suppress rain streaks in a coarse-to-fine manner, while a Fourier Shaped Attention Module (FSAM) is introduced, in which Rectangular Filter Attention (RFA) and Square Filter Attention (SFA) are used to enhance direction-sensitive and local-structure-sensitive frequency representations, respectively. Furthermore, a dual-domain loss is employed to jointly constrain spatial structural restoration and frequency-domain consistency, thereby improving restoration quality in complex scenes. Experimental results show that SynFSNet achieves superior performance on multiple synthetic and real-world datasets, verifying the effectiveness of collaborative spatial-frequency modeling for complex image deraining tasks.

摘要： 图像去雨旨在从受雨迹干扰的退化图像中恢复高质量的无雨图像，是提升户外视觉系统鲁棒性的关键技术。现有基于卷积神经网络的方法受限于局部感受野，难以有效建模长程依赖关系；部分基于 Transformer 的方法虽增强了全局建模能力，但对复杂雨迹的多尺度、方向性及局部高频细节恢复仍存在不足；已有频域增强方法也多将频域作为辅助表示手段，缺乏针对空间域与频域互补关系的细粒度协同建模。为此，本文提出了一种频域与空间域协同建模的图像去雨网络（Synergizing Frequency and Spatial Network，SynFSNet）。不同于现有方法主要将频域作为全局增强工具，本文从雨迹在频域中的方向性、密度性和尺度变化出发，通过多尺度傅里叶融合模块（Multi-Scale Fourier Fusion Module，MSFFM）实现由粗到细的渐进式雨迹抑制，并通过傅里叶形状注意模块（Fourier Shaped Attention Module，FSAM）中的矩形滤波器注意力（Rectangular Filter Attention，RFA）和方形滤波器注意力（Square Filter Attention，SFA）分别增强方向敏感与局部结构敏感的频域表示。进一步地，网络通过双域损失函数同时约束空间域结构与频域一致性，以提升复杂场景下的恢复质量。实验结果表明，SynFSNet 在多个合成数据集和真实场景数据集上均取得了较优性能，验证了频域与空间域协同建模在复杂图像去雨任务中的有效性。