Recursive Multi-Scale Network With Enhanced Attention for Structure-Preserving Deraining
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 im...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10771756/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |