MFAENet: A Multiscale Feature Adaptive Enhancement Network for SAR Image Despeckling

doi:10.1109/JSTARS.2023.3327332

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	MFAENet: A Multiscale Feature Adaptive Enhancement Network for SAR Image Despeckling
	Liu, Shuaiqi 1,2; Zhang, Luyao 1; Tian, Shikang 1; Hu, Qi 3; Li, Bing 2; Zhang, Yudong 4
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	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
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001123950300010
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
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.