MFAENet: A Multiscale Feature Adaptive Enhancement Network for SAR Image Despeckling
文献类型：期刊论文
| 作者 | Liu, Shuaiqi1,2; Zhang, Luyao1; Tian, Shikang1; Hu, Qi3; Li, Bing2 ; Zhang, Yudong4
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| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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| 出版日期 | 2023 |
| 卷号 | 16页码:10420-10433 |
| 关键词 | Adaptive fusion feature enhancement multiscale feature speckle suppression synthetic aperture radar (SAR) images |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2023.3327332 |
| 通讯作者 | Hu, Qi(qihu_hbu@163.com) |
| 英文摘要 | The existence of speckles in synthetic aperture radar (SAR) images affects its subsequent application in computer vision tasks, so the research of speckle suppression plays a very important role. Convolutional neural networks based speckle suppression algorithms cannot reach a good balance between despeckling effect and structure detail preservation. Considering these issues, a multiscale feature adaptive enhance network for suppressing speckle is proposed. Specifically, an encoder-decoder architecture embedded with multiscale operations is constructed to capture rich contextual information and remove speckles from coarse to fine. Then, deformable convolution is introduced to flexibly adapt changes in ground objects' complex and diverse image features. Also, the constructed feature adaptive mixup module mitigates shallow feature degradation in deep networks by establishing connections between shallow image texture features and deep image semantic features with learnable weights. Experiments on synthetic and real SAR images show that the proposed method produces advanced results regarding visual quality and objective metrics. |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001123950300010 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54898]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Hu, Qi |
| 作者单位 | 1.Hebei Univ, Coll Elect & Informat Engn, Machine Vis Technol Innovat Ctr Hebei Prov, Baoding 071002, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China 4.Univ Leicester, Sch Comp & Math, Leicester LE1 7RH, England |
| 推荐引用方式 GB/T 7714 | Liu, Shuaiqi,Zhang, Luyao,Tian, Shikang,et al. MFAENet: A Multiscale Feature Adaptive Enhancement Network for SAR Image Despeckling[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2023,16:10420-10433. |
| APA | Liu, Shuaiqi,Zhang, Luyao,Tian, Shikang,Hu, Qi,Li, Bing,&Zhang, Yudong.(2023).MFAENet: A Multiscale Feature Adaptive Enhancement Network for SAR Image Despeckling.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,16,10420-10433. |
| MLA | Liu, Shuaiqi,et al."MFAENet: A Multiscale Feature Adaptive Enhancement Network for SAR Image Despeckling".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16(2023):10420-10433. |
入库方式： OAI收割
来源：自动化研究所
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