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.

我们开发了一种创新的双路径耦合去雨网络（DPCNet），通过空间特征提取块（SFEBlock）和频率特征提取块（FFEBlock）在空间和频率域中整合信息，并引入了一种有效的自适应融合模块（AFM）来进行双通路径特征聚合。该方法在六个公共去雨基准和下游视觉任务上进行了大量实验证明，不仅超越了现有的最先进去雨方法，而且在下游视觉任务中具有出色的鲁棒性和视觉效果。