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MRDDANet: A Multiscale Residual Dense Dual Attention Network for SAR Image Denoising

文献类型：期刊论文

作者Liu, Shuaiqi2; Lei, Yu4; Zhang, Luyao4; Li, Bing3; Hu, Weiming3; Zhang, Yu-Dong1
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2021-08-31
页码13
关键词Noise reduction Radar polarimetry Feature extraction Speckle Transforms Synthetic aperture radar Image denoising Dual attention network feature extraction multiscale synthetic aperture radar (SAR) image denoising
ISSN号0196-2892
DOI10.1109/TGRS.2021.3106764
通讯作者Hu, Weiming(wmhu@nlpr.ia.ac.cn)
英文摘要Synthetic aperture radar (SAR), due to its inherent characteristics, will produce speckle noise, which results in the deterioration of image quality, so the removal of speckle in SAR image is very important for the subsequent high-level image processing. In order to balance the relationship between denoising and texture preservation, we propose a multiscale residual dense dual attention network (MRDDANet) for SAR image denoising. This algorithm can effectively suppress the speckle while fully retaining the texture details of the image. In MRDDANet, shallow features are extracted from the noisy images by multiscale modules with different kernel sizes, and then, the extracted shallow features are mapped to the residual dense dual-attention network to obtain the deep features of SAR image. Finally, the final denoising image is generated through global residual learning. MRDDANet has advantages of both multiscale blocks and residual dense dual attention networks. The dense connection can fully extract features in the image, and the dual-channel attention enables MRDDANet to pay more attention to noise information, which is beneficial to remove noise and keep the details of the original image at the same time. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only improves various objective indicators but also shows great advantages in visual effects.
WOS关键词QUALITY ASSESSMENT ; TRANSFORM ; SHRINKAGE ; WAVELET ; FILTER ; NOISE
资助项目National Natural Science Foundation of China[62172139] ; National Natural Science Foundation of China[61401308] ; Natural Science Foundation of Hebei Province[F2020201025] ; Natural Science Foundation of Hebei Province[F2019201151] ; Natural Science Foundation of Hebei Province[F2018210148] ; Science Research Project of Hebei Province[BJ2020030] ; Science Research Project of Hebei Province[QN2017306] ; Open Foundation of Guangdong Key Laboratory of Digital Signal and Image Processing Technology[2020GDDSIPL-04] ; High-Performance Computing Center of Hebei University
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000732766300001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province ; Science Research Project of Hebei Province ; Open Foundation of Guangdong Key Laboratory of Digital Signal and Image Processing Technology ; High-Performance Computing Center of Hebei University
源URL[http://ir.ia.ac.cn/handle/173211/46935]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
通讯作者Hu, Weiming
作者单位1.Univ Leicester, Dept Informat, Leicester LE1 7RH, Leics, England
2.Hebei Univ, Acad Sci, Coll Elect & Informat Engn, Machine Vis Engn Res Ctr Hebei Prov, Baoding 071002, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China
4.Hebei Univ, Coll Elect & Informat Engn, Key Lab Digital Med Engn Hebei Prov, Baoding 071002, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Shuaiqi,Lei, Yu,Zhang, Luyao,et al. MRDDANet: A Multiscale Residual Dense Dual Attention Network for SAR Image Denoising[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021:13.
APA Liu, Shuaiqi,Lei, Yu,Zhang, Luyao,Li, Bing,Hu, Weiming,&Zhang, Yu-Dong.(2021).MRDDANet: A Multiscale Residual Dense Dual Attention Network for SAR Image Denoising.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,13.
MLA Liu, Shuaiqi,et al."MRDDANet: A Multiscale Residual Dense Dual Attention Network for SAR Image Denoising".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021):13.

入库方式： OAI收割

来源：自动化研究所

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