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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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10771756/ |
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| author | Shuang Jiao Jianping Zhao Hua Li Yun Yang Ning Gu |
| author_facet | Shuang Jiao Jianping Zhao Hua Li Yun Yang Ning Gu |
| author_sort | Shuang Jiao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b49ee9570ed24f5d93790ac83098ae7b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b49ee9570ed24f5d93790ac83098ae7b2024-12-24T00:01:14ZengIEEEIEEE Access2169-35362024-01-011218171818172710.1109/ACCESS.2024.350880510771756Recursive Multi-Scale Network With Enhanced Attention for Structure-Preserving DerainingShuang Jiao0https://orcid.org/0009-0009-9656-222XJianping Zhao1https://orcid.org/0000-0003-0824-538XHua Li2https://orcid.org/0000-0002-8566-1558Yun Yang3Ning Gu4School of Computer Science and Technology, Changchun University of Science and Technology, Chaoyang, Changchun, Jilin, ChinaSchool of Computer Science and Technology, Changchun University of Science and Technology, Chaoyang, Changchun, Jilin, ChinaSchool of Computer Science and Technology, Changchun University of Science and Technology, Chaoyang, Changchun, Jilin, ChinaChangchun Institute of Education, Luyuan, Changchun, Jilin, ChinaChangchun Institute of Education, Luyuan, Changchun, Jilin, ChinaRain 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.https://ieeexplore.ieee.org/document/10771756/Recursivemulti-scalestructure-preservingderainingmulti-scale feature processingimage structure preservation |
| spellingShingle | Shuang Jiao Jianping Zhao Hua Li Yun Yang Ning Gu Recursive Multi-Scale Network With Enhanced Attention for Structure-Preserving Deraining IEEE Access Recursive multi-scale structure-preserving deraining multi-scale feature processing image structure preservation |
| title | Recursive Multi-Scale Network With Enhanced Attention for Structure-Preserving Deraining |
| title_full | Recursive Multi-Scale Network With Enhanced Attention for Structure-Preserving Deraining |
| title_fullStr | Recursive Multi-Scale Network With Enhanced Attention for Structure-Preserving Deraining |
| title_full_unstemmed | Recursive Multi-Scale Network With Enhanced Attention for Structure-Preserving Deraining |
| title_short | Recursive Multi-Scale Network With Enhanced Attention for Structure-Preserving Deraining |
| title_sort | recursive multi scale network with enhanced attention for structure preserving deraining |
| topic | Recursive multi-scale structure-preserving deraining multi-scale feature processing image structure preservation |
| url | https://ieeexplore.ieee.org/document/10771756/ |
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