Journal
INFRARED PHYSICS & TECHNOLOGY
Volume 77, Issue -, Pages 421-430Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.infrared.2016.06.021
Keywords
Infrared image; Target and background separation; Weighted infrared patch-image model; Column-wise weighted RPCA; Target unlikelihood coefficient
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Funding
- National Natural Science Foundation of China [61573183]
- Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences [LSIT201401]
- Open Fund of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University) [PLN1303]
- Open Fund of State Key Laboratory of Marine Geology, Tongji University [MGK1412]
- Foundation of Graduate Innovation Center in NUAA [kfjj201430]
- Fundamental Research Funds for the Central Universities
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When facing extremely complex infrared background, due to the defect of 11 norm based sparsity measure, the state-of-the-art infrared patch-image (IPI) model would be in a dilemma where either the dim targets are over-shrinked in the separation or the strong cloud edges remains in the target image. In order to suppress the strong edges while preserving the dim targets, a weighted infrared patch image (WIPI) model is proposed, incorporating structural prior information into the process of infrared small target and background separation. Instead of adopting a global weight, we allocate adaptive weight to each column of the target patch-image according to its patch structure. Then the proposed WIPI model is converted to a column-wise weighted robust principal component analysis (CWRPCA) problem. In addition, a target unlikelihood coefficient is designed based on the steering kernel, serving as the adaptive weight for each column. Finally, in order to solve the CWPRCA problem, a solution algorithm is developed based on Alternating Direction Method (ADM). Detailed experiment results demonstrate that the proposed method has a significant improvement over the other nine classical or state-of-the-art methods in terms of subjective visual quality, quantitative evaluation indexes and convergence rate. (C) 2016 Elsevier B.V. All rights reserved.
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