  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.
