FRANet: A Feature Refinement Attention Network for SAR Image Denoising
Since synthetic aperture radar (SAR) images have complex noise and have no clean reference images, SAR image denoising is very challenging. With the development of deep learning, several denoising algorithms based on deep learning are proposed to achieve a better SAR image denoising effect. However,...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10979213/ |
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| _version_ | 1850268419560046592 |
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| author | Shuaiqi Liu Yu Lei Qi Hu Ming Liu Bing Li Weiming Hu Yu-Dong Zhang |
| author_facet | Shuaiqi Liu Yu Lei Qi Hu Ming Liu Bing Li Weiming Hu Yu-Dong Zhang |
| author_sort | Shuaiqi Liu |
| collection | DOAJ |
| description | Since synthetic aperture radar (SAR) images have complex noise and have no clean reference images, SAR image denoising is very challenging. With the development of deep learning, several denoising algorithms based on deep learning are proposed to achieve a better SAR image denoising effect. However, most networks are prone to gradient disappearance and explosion in the training process. The deep network model will produce an excessive amount of computation. The denoising time is also too long. Since most of the denoising algorithms based on deep learning use simulated images for model training, it is difficult to effectively suppress speckle noise in the real SAR image while a balance between denoising and detail preservation cannot be achieved. To address the mentioned problems, we propose a novel feature refinement attention network named FRANet. In FRANet, a feature refinement network is first used to refine the input noise image to extract more useful features while accelerating network training. Second, a feature attention encoder–decoder network is constructed for deep feature extraction. This network uses an asymmetric encoder–decoder structure to expand the receptive field, which can improve the information extraction ability and reduce the number of parameters effectively. Finally, the final denoised SAR image is obtained by global residual learning. Compared with other denoising algorithms, the proposed algorithm can achieve better results in denoising performance and running time. |
| format | Article |
| id | doaj-art-5c07b3199eac47f1bf7e16b2322f38c7 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-5c07b3199eac47f1bf7e16b2322f38c72025-08-20T01:53:30ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118123431236310.1109/JSTARS.2025.356484610979213FRANet: A Feature Refinement Attention Network for SAR Image DenoisingShuaiqi Liu0https://orcid.org/0000-0001-7520-8226Yu Lei1https://orcid.org/0000-0003-3392-9441Qi Hu2https://orcid.org/0000-0002-7495-1066Ming Liu3https://orcid.org/0009-0002-9258-0737Bing Li4https://orcid.org/0000-0002-5888-6735Weiming Hu5https://orcid.org/0000-0001-9237-8825Yu-Dong Zhang6https://orcid.org/0000-0002-4870-1493College of Electronic and Information Engineering, Machine Vision Engineering Research Center of Hebei Province, Hebei University, Baoding, ChinaCollege of Electronic and Information Engineering, Machine Vision Engineering Research Center of Hebei Province, Hebei University, Baoding, ChinaCollege of Electronic and Information Engineering, Machine Vision Engineering Research Center of Hebei Province, Hebei University, Baoding, ChinaEducation and Teaching Research and Teacher Training Promotion Center, Hebei University, Baoding, ChinaNational Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaNational Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaSchool of Computing and Mathematical Science, University of Leicester, Leicester, U.K.Since synthetic aperture radar (SAR) images have complex noise and have no clean reference images, SAR image denoising is very challenging. With the development of deep learning, several denoising algorithms based on deep learning are proposed to achieve a better SAR image denoising effect. However, most networks are prone to gradient disappearance and explosion in the training process. The deep network model will produce an excessive amount of computation. The denoising time is also too long. Since most of the denoising algorithms based on deep learning use simulated images for model training, it is difficult to effectively suppress speckle noise in the real SAR image while a balance between denoising and detail preservation cannot be achieved. To address the mentioned problems, we propose a novel feature refinement attention network named FRANet. In FRANet, a feature refinement network is first used to refine the input noise image to extract more useful features while accelerating network training. Second, a feature attention encoder–decoder network is constructed for deep feature extraction. This network uses an asymmetric encoder–decoder structure to expand the receptive field, which can improve the information extraction ability and reduce the number of parameters effectively. Finally, the final denoised SAR image is obtained by global residual learning. Compared with other denoising algorithms, the proposed algorithm can achieve better results in denoising performance and running time.https://ieeexplore.ieee.org/document/10979213/Attention encoder–decoder networkdeep learningfeature refinementimage denoisingsynthetic aperture radar (SAR) |
| spellingShingle | Shuaiqi Liu Yu Lei Qi Hu Ming Liu Bing Li Weiming Hu Yu-Dong Zhang FRANet: A Feature Refinement Attention Network for SAR Image Denoising IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Attention encoder–decoder network deep learning feature refinement image denoising synthetic aperture radar (SAR) |
| title | FRANet: A Feature Refinement Attention Network for SAR Image Denoising |
| title_full | FRANet: A Feature Refinement Attention Network for SAR Image Denoising |
| title_fullStr | FRANet: A Feature Refinement Attention Network for SAR Image Denoising |
| title_full_unstemmed | FRANet: A Feature Refinement Attention Network for SAR Image Denoising |
| title_short | FRANet: A Feature Refinement Attention Network for SAR Image Denoising |
| title_sort | franet a feature refinement attention network for sar image denoising |
| topic | Attention encoder–decoder network deep learning feature refinement image denoising synthetic aperture radar (SAR) |
| url | https://ieeexplore.ieee.org/document/10979213/ |
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