Transformers have recently emerged as a significant force in the field of image deraining. Existing image deraining methods utilize extensive research on self-attention. Though showcasing impressive results, they tend to neglect critical frequency information, as self-attention is generally less adept at capturing high-frequency details. To overcome this shortcoming, we have developed an innovative Dual-Path Coupled Deraining Network (DPCNet) that integrates information from both spatial and frequency domains through Spatial Feature Extraction Block (SFEBlock) and Frequency Feature Extraction Block (FFEBlock). We have further introduced an effective Adaptive Fusion Module (AFM) for the dual-path feature aggregation. Extensive experiments on six public deraining benchmarks and downstream vision tasks have demonstrated that our proposed method not only outperforms the existing state-of-the-art deraining method but also achieves visually pleasuring results with excellent robustness on downstream vision tasks.

翻译：Transformer近年来在图像去雨领域展现出重要影响力。现有图像去雨方法广泛研究了自注意力机制，虽然取得了显著成果，但由于自注意力通常难以有效捕捉高频细节，这些方法往往忽略了关键的频率信息。为克服这一缺陷，我们提出了一种创新的双路径耦合去雨网络（DPCNet），该网络通过空间特征提取模块（SFEBlock）和频率特征提取模块（FFEBlock）整合空间域与频率域信息。我们进一步引入了有效的自适应融合模块（AFM），用于双路径特征聚合。在六个公开去雨基准数据集及下游视觉任务上的大量实验表明，所提方法不仅超越了现有最先进的去雨方法，还在下游视觉任务上取得了视觉效果优异且鲁棒性出色的结果。