From heavy rain removal to detail restoration: A faster and better network
The profound accumulation of precipitation during intense rainfall events can markedly degrade the quality of images, leading to the erosion of textural details. Despite the improvements observed in existing learning-based methods specialized for heavy rain removal, it is discerned that a significan...
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| Published in: | Pattern recognition Vol. 148; p. 110205 |
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01.04.2024
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| ISSN: | 0031-3203 |
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| Abstract | The profound accumulation of precipitation during intense rainfall events can markedly degrade the quality of images, leading to the erosion of textural details. Despite the improvements observed in existing learning-based methods specialized for heavy rain removal, it is discerned that a significant proportion of these methods tend to overlook the precise reconstruction of the intricate details. In this work, we introduce a simple dual-stage progressive enhancement network, denoted as DPENet, aiming to achieve effective deraining while preserving the structural accuracy of rain-free images. This approach comprises two key modules, a rain streaks removal network (R2Net) focusing on accurate rain removal, and a details reconstruction network (DRNet) designed to recover the textural details of rain-free images. Firstly, we introduce a dilated dense residual block (DDRB) within R2Net, enabling the aggregation of high-level and low-level features. Secondly, an enhanced residual pixel-wise attention block (ERPAB) is integrated into DRNet to facilitate the incorporation of contextual information. To further enhance the fidelity of our approach, we employ a comprehensive loss function that accentuates both the marginal and regional accuracy of rain-free images. Extensive experiments conducted on publicly available benchmarks demonstrates the noteworthy efficiency and effectiveness of our proposed DPENet. The source code and pre-trained models are currently available at https://github.com/chdwyb/DPENet.
•Dual-stage deraining network performs better than the single-stage network.•Multi-level feature fusion is significant for rain streaks removal.•Contextual information aggregation facilitates textural details reconstruction. |
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| AbstractList | The profound accumulation of precipitation during intense rainfall events can markedly degrade the quality of images, leading to the erosion of textural details. Despite the improvements observed in existing learning-based methods specialized for heavy rain removal, it is discerned that a significant proportion of these methods tend to overlook the precise reconstruction of the intricate details. In this work, we introduce a simple dual-stage progressive enhancement network, denoted as DPENet, aiming to achieve effective deraining while preserving the structural accuracy of rain-free images. This approach comprises two key modules, a rain streaks removal network (R2Net) focusing on accurate rain removal, and a details reconstruction network (DRNet) designed to recover the textural details of rain-free images. Firstly, we introduce a dilated dense residual block (DDRB) within R2Net, enabling the aggregation of high-level and low-level features. Secondly, an enhanced residual pixel-wise attention block (ERPAB) is integrated into DRNet to facilitate the incorporation of contextual information. To further enhance the fidelity of our approach, we employ a comprehensive loss function that accentuates both the marginal and regional accuracy of rain-free images. Extensive experiments conducted on publicly available benchmarks demonstrates the noteworthy efficiency and effectiveness of our proposed DPENet. The source code and pre-trained models are currently available at https://github.com/chdwyb/DPENet.
•Dual-stage deraining network performs better than the single-stage network.•Multi-level feature fusion is significant for rain streaks removal.•Contextual information aggregation facilitates textural details reconstruction. |
| ArticleNumber | 110205 |
| Author | Zhang, Jing Wen, Yuanbo Chen, Ting Zhang, Kaihao Gao, Tao |
| Author_xml | – sequence: 1 givenname: Yuanbo orcidid: 0000-0001-7599-5645 surname: Wen fullname: Wen, Yuanbo organization: School of Information Engineering, Chang’an University, Xi’an 710064, China – sequence: 2 givenname: Tao orcidid: 0000-0002-5750-7449 surname: Gao fullname: Gao, Tao email: tgaochd@126.com organization: School of Information Engineering, Chang’an University, Xi’an 710064, China – sequence: 3 givenname: Jing orcidid: 0000-0002-8516-0913 surname: Zhang fullname: Zhang, Jing organization: School of Computing, Australian National University, Canberra, ACT 2600, Australia – sequence: 4 givenname: Kaihao orcidid: 0000-0002-4317-660X surname: Zhang fullname: Zhang, Kaihao organization: School of Computing, Australian National University, Canberra, ACT 2600, Australia – sequence: 5 givenname: Ting orcidid: 0000-0002-8134-6913 surname: Chen fullname: Chen, Ting organization: School of Information Engineering, Chang’an University, Xi’an 710064, China |
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| Keywords | Dual-stage network Dilated convolution Single image deraining Pixel-wise attention Detail reconstruction |
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