双路径耦合图像去雨网络：基于空间-频率交互
Dual-Path Coupled Image Deraining Network via Spatial-Frequency Interaction
AI总结：
提出双路径耦合去雨网络DPCNet，通过空间与频率特征提取块及自适应融合模块，在六个基准上超越现有最先进方法，并提升下游视觉任务鲁棒性。
摘要：
近年来，Transformer在图像去雨领域已成为一股重要力量。现有的图像去雨方法广泛研究了自注意力机制。尽管这些方法展示了令人印象深刻的结果，但它们往往忽略了关键的频率信息，因为自注意力机制通常不太擅长捕捉高频细节。为了克服这一缺陷，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）整合来自空间域和频率域的信息。我们进一步引入了一个有效的自适应融合模块（AFM）用于双路径特征聚合。在六个公开的去雨基准数据集和下游视觉任务上的大量实验表明，我们提出的方法不仅优于现有的最先进去雨方法，而且在下游视觉任务上取得了视觉上令人愉悦的结果和出色的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
