RGPNet: Learning a detail-preserved progressive model for single image deraining based on gradient prior
The issue of rain removal in images is challenging due to the diverse rain distributions and complex backgrounds. Although deraining capabilities have greatly improved in the previous single-stage approaches, background detail loss is a common result. To effectively remove rain that is diversly dist...
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| Published in: | Digital signal processing Vol. 165; p. 105312 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.10.2025
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| ISSN: | 1051-2004 |
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| Abstract | The issue of rain removal in images is challenging due to the diverse rain distributions and complex backgrounds. Although deraining capabilities have greatly improved in the previous single-stage approaches, background detail loss is a common result. To effectively remove rain that is diversly distributed, many methods adopt a multistage rain removal framework for progressive deraining. Most of these multistage methods repeatedly use information from previous stages, or the original rainy image to refine the rain extracted from the background, leading to high demands on the ability to discriminate the background structure from the rain. To address this problem, we introduce a gradient prior to artificially preserve the background details. In addition, an attention LSTM is proposed to reduce the artifacts caused by over-deraining, by focusing on the more visible rain regions. The overall architecture of the proposed method consists of a rain streak extraction branch and a background detail recovery branch, with the designed attention LSTM and the proposed gradient prior integrated into the former and latter branches, respectively. Experiments on several well-known benchmark datasets show that our methods can outperform many state-of-the-art methods. |
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| AbstractList | The issue of rain removal in images is challenging due to the diverse rain distributions and complex backgrounds. Although deraining capabilities have greatly improved in the previous single-stage approaches, background detail loss is a common result. To effectively remove rain that is diversly distributed, many methods adopt a multistage rain removal framework for progressive deraining. Most of these multistage methods repeatedly use information from previous stages, or the original rainy image to refine the rain extracted from the background, leading to high demands on the ability to discriminate the background structure from the rain. To address this problem, we introduce a gradient prior to artificially preserve the background details. In addition, an attention LSTM is proposed to reduce the artifacts caused by over-deraining, by focusing on the more visible rain regions. The overall architecture of the proposed method consists of a rain streak extraction branch and a background detail recovery branch, with the designed attention LSTM and the proposed gradient prior integrated into the former and latter branches, respectively. Experiments on several well-known benchmark datasets show that our methods can outperform many state-of-the-art methods. |
| ArticleNumber | 105312 |
| Author | Zhou, Teng Huang, Huiwu Liang, Gaoquan Ling, Jie Han, Tian Luo, Yu |
| Author_xml | – sequence: 1 givenname: Yu surname: Luo fullname: Luo, Yu email: yuluo@gdut.edu.cn organization: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, Guangdong province, China – sequence: 2 givenname: Gaoquan orcidid: 0009-0009-7236-8721 surname: Liang fullname: Liang, Gaoquan email: lianggaoquan@gmail.com organization: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, Guangdong province, China – sequence: 3 givenname: Jie surname: Ling fullname: Ling, Jie email: jling@gdut.edu.cn organization: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, Guangdong province, China – sequence: 4 givenname: Teng surname: Zhou fullname: Zhou, Teng email: teng.zhou@hainanu.edu.cn organization: School of Cyberspace Security (School of Cryptology), Hainan University, Haikou, 570228, Hainan province, China – sequence: 5 givenname: Huiwu orcidid: 0009-0000-2626-2998 surname: Huang fullname: Huang, Huiwu email: hedy@gdut.edu.cn organization: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, Guangdong province, China – sequence: 6 givenname: Tian surname: Han fullname: Han, Tian email: than6@stevens.edu organization: Department of Computer Science, Stevens Institute of Technology, Hoboken, 07030, NJ, United States |
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| Keywords | Computer vision Gradient prior Image deraining |
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| Title | RGPNet: Learning a detail-preserved progressive model for single image deraining based on gradient prior |
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