Wavelet-driven multi-frequency signal unlocking network for image deraining
Rain streaks and mist caused by rainfall can significantly degrade image quality, directly hindering downstream tasks. In recent years, wavelet transform-based rain removal methods have advanced due to their ability to capture frequency characteristics, thereby enhancing image structure preservation...
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| Published in: | Neurocomputing (Amsterdam) Vol. 652; p. 131089 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.11.2025
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| ISSN: | 0925-2312 |
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| Abstract | Rain streaks and mist caused by rainfall can significantly degrade image quality, directly hindering downstream tasks. In recent years, wavelet transform-based rain removal methods have advanced due to their ability to capture frequency characteristics, thereby enhancing image structure preservation while suppressing high-frequency noise. However, existing approaches often overlook the importance of independently processing rain components, disrupting the intrinsic relationship between rain and the background. To address this limitation, we propose a novel framework called the wavelet-driven multi-frequency signal unlocking (WD-MFSU) network. This network employs a wavelet decomposition-driven multi-element learning (WDMEL) mechanism that integrates frequency signals into an attention mechanism and differential learning through a collaborative processing mode, which includes global, structural, and detail-oriented branches. This approach alleviates artifacts and background distortion during rain removal. Additionally, we introduce the rain-background augmented dynamic interaction (RBADC) module to enhance cross-component feature interaction and capture potential dependencies between rain and the background. By utilizing sequential compound attention and dynamic convolution, this module recalibrates channels for rain and background, facilitating dynamic feature adjustments. Comprehensive evaluations demonstrate that our method excels across multiple rainy datasets, outperforming recently proposed approaches while maintaining lower complexity, thus showcasing its effectiveness and generalizability in real-world rainy scenes and downstream tasks. The source code will be available at https://github.com/ChenHuyoo. |
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| AbstractList | Rain streaks and mist caused by rainfall can significantly degrade image quality, directly hindering downstream tasks. In recent years, wavelet transform-based rain removal methods have advanced due to their ability to capture frequency characteristics, thereby enhancing image structure preservation while suppressing high-frequency noise. However, existing approaches often overlook the importance of independently processing rain components, disrupting the intrinsic relationship between rain and the background. To address this limitation, we propose a novel framework called the wavelet-driven multi-frequency signal unlocking (WD-MFSU) network. This network employs a wavelet decomposition-driven multi-element learning (WDMEL) mechanism that integrates frequency signals into an attention mechanism and differential learning through a collaborative processing mode, which includes global, structural, and detail-oriented branches. This approach alleviates artifacts and background distortion during rain removal. Additionally, we introduce the rain-background augmented dynamic interaction (RBADC) module to enhance cross-component feature interaction and capture potential dependencies between rain and the background. By utilizing sequential compound attention and dynamic convolution, this module recalibrates channels for rain and background, facilitating dynamic feature adjustments. Comprehensive evaluations demonstrate that our method excels across multiple rainy datasets, outperforming recently proposed approaches while maintaining lower complexity, thus showcasing its effectiveness and generalizability in real-world rainy scenes and downstream tasks. The source code will be available at https://github.com/ChenHuyoo. |
| ArticleNumber | 131089 |
| Author | Cao, Shaohan Liu, Jiping Feng, Dejun Zhu, Jun Xie, Yakun Chen, Hongyu Wang, Yong |
| Author_xml | – sequence: 1 givenname: Jiping surname: Liu fullname: Liu, Jiping email: liujp@casm.ac.cn organization: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China – sequence: 2 givenname: Hongyu orcidid: 0009-0009-2242-1301 surname: Chen fullname: Chen, Hongyu email: chy0519@my.swjtu.edu.cn organization: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China – sequence: 3 givenname: Shaohan surname: Cao fullname: Cao, Shaohan email: 17516390760@163.com organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China – sequence: 4 givenname: Yong surname: Wang fullname: Wang, Yong email: wangyong@casm.ac.cn organization: Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100036, China – sequence: 5 givenname: Jun surname: Zhu fullname: Zhu, Jun email: zhujun@swjtu.edu.cn organization: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China – sequence: 6 givenname: Dejun surname: Feng fullname: Feng, Dejun email: djfeng@swjtu.edu.cn organization: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China – sequence: 7 givenname: Yakun surname: Xie fullname: Xie, Yakun email: yakunxie@163.com organization: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China |
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| Keywords | Feature Interaction Image deraining Wavelet transform Multi-frequency learning |
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