Dynamic coarse‐to‐fine ISAR image blind denoising using active joint prior learning

International Journal of Intelligent SystemsVolume 36, Issue 8 p. 4143-4166 RESEARCH ARTICLE Dynamic coarse-to-fine ISAR image blind denoising using active joint prior learning Bin Xue, Corresponding Author Bin Xue [email protected] orcid.org/0000-0001-5068-9932 National University of Defense Technology, School of Information and Communication, Xi'an, China Correspondence Bin Xue, National University of Defense Technology, School of Information and Communication, 710106 Xi'an, China. Email: [email protected]Search for more papers by this authorYi He, Yi He National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this authorFeng Jing, Feng Jing orcid.org/0000-0001-7061-8162 National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this authorYimeng Ren, Yimeng Ren orcid.org/0000-0003-1004-1829 Renmin University of China, School of Statistics, Beijing, ChinaSearch for more papers by this authorMei Gao, Mei Gao orcid.org/0000-0002-1781-6132 National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this author Bin Xue, Corresponding Author Bin Xue [email protected] orcid.org/0000-0001-5068-9932 National University of Defense Technology, School of Information and Communication, Xi'an, China Correspondence Bin Xue, National University of Defense Technology, School of Information and Communication, 710106 Xi'an, China. Email: [email protected]Search for more papers by this authorYi He, Yi He National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this authorFeng Jing, Feng Jing orcid.org/0000-0001-7061-8162 National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this authorYimeng Ren, Yimeng Ren orcid.org/0000-0003-1004-1829 Renmin University of China, School of Statistics, Beijing, ChinaSearch for more papers by this authorMei Gao, Mei Gao orcid.org/0000-0002-1781-6132 National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this author First published: 19 May 2021 https://doi.org/10.1002/int.22454Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Most existing nonblind denoising approaches assumed the noise to be homogeneous white Gaussian distribution with known intensity. However, it is difficult to know beforehand or model accurately real-world noises with complex hybrid distribution and noise intensity. In this paper, active joint prior learning (JPL) is proposed for real-world ISAR image blind denoising. (1) To explore strong model hierarchy and components relationship automatically, a novel graphical Dirichlet mixture process (GDMP) model is developed, where the latent representations and component hyperparameters are jointly learned from each other. (2) A multiscale joint learning strategy (MJLS) is proposed to take advantage of both the optimization- and discriminative learning-based capabilities. The external noiseless, internal noisy image information and their relationships are jointly explored simultaneously. (3) Low-rank weighted sparse learning (LWSL) is proposed to learn sparse discriminative correlation components for robust prior learning, and latent low-rank embedding for GDMP patterns self-adaptive inference. Extensive experimental results on ISAR image datasets demonstrate the effectiveness of the proposed model for both synthesis and real-world noisy ISAR images, and the proposed method outperforms the state-of-the-art denoising methods. Citing Literature Volume36, Issue8August 2021Pages 4143-4166 RelatedInformation