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Proceedings Paper

Microwave staring correlated imaging via the combination of nonconvex low-rank and total variation regularization
Author(s): Guochao Wang; Bo Yuan; Guanghua Lu
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Paper Abstract

Based on the temporal-spatial stochastic radiation field (TSSRF), microwave staring correlated imaging (MSCI) can achieve high resolution images of the targets. This paper focuses on the nonlocal and local regularization to improve the MSCI reconstruction performance. Low-rank regularization is considered to reveal the global information of the images. For local, total variation is a typical choice for its excellent edge preserving ability and noise reduction. Therefore, a method with the combination of the low-rank and total variation regularization is considered in this paper, in which a logarithmic function is taken as a non-convex approximation of the rank function. By elaborately adjusting the regularization parameters, the whole problem is still convex. Thus the Split Bregman method is utilized to solve the convex optimization problem and form the proposed algorithm, namely the LogRankTV algorithm. The effectiveness of the LogRankTV algorithm is demonstrated via the simulation results.

Paper Details

Date Published: 9 August 2018
PDF: 9 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108063J (9 August 2018); doi: 10.1117/12.2502940
Show Author Affiliations
Guochao Wang, Univ. of Science and Technology of China (China)
Bo Yuan, Univ. of Science and Technology of China (China)
Guanghua Lu, Univ. of Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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