Recursive Multi-Scale Network With Enhanced Attention for Structure-Preserving Deraining
Shuang Jiao, Jianping Zhao, Hua Li, Yun Jin Yang, Ning Gu · IEEE Access · 2024
Rain streaks in outdoor images can severely affect the performance of high-level vision tasks, such as surveillance and autonomous driving, by obscuring important image details. The objective of this research is to develop a more effective deraining method that preserves the structural details of images while removing rain streaks. To address the limitations of existing methods, we introduce the Recursive Multi-Scale Network with Enhanced Attention for Structure-Preserving Deraining (RMASP-Net). This network integrates multi-scale feature processing, recursive architecture, attention mechanisms, and Rectified Local Contrast Normalization (RLCN). It utilizes wavelet transform’s scale-space capabilities to detect key features across different scales, improving the alignment of structural features and isolating rain pixels effectively. Additionally, RMASP-Net features a Mask-Guided Attention Module (Mask-GAM) for dynamic feature weighting and an Interactive Fusion Module (IFM) to enhance image clarity and structure. Extensive experiments on both synthetic and real-world datasets demonstrate that RMASP-Net significantly outperforms existing methods. Specifically, RMASP-Net shows noticeable improvements in both PSNR and SSIM across datasets like Rain100H, Rain100L, and Test1200, providing clearer and more structurally accurate derained images. This research sets a new benchmark for deraining performance under real-world conditions.