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Article

Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy

by 1,2, 1,2, 1,2 and 1,2,3,*
1
School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
2
Laboratory of Imaging Detection and Intelligent Perception, UESTC, Chengdu 610054, China
3
Center for Information Geoscience, UESTC, Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Received: 2 April 2020 / Revised: 6 May 2020 / Accepted: 6 May 2020 / Published: 9 May 2020
(This article belongs to the Special Issue Computer Vision and Machine Learning Application on Earth Observation)
Small target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is proposed. To improve the latest infrared patch-tensor (IPT) model, a non-convex tensor rank surrogate merging tensor nuclear norm (TNN) and the Laplace function, is utilized for low rank background patch-tensor constraint, which has a useful property of adaptively allocating weight for every singular value and can better approximate l 0 -norm. Considering that the local prior map can be equivalent to the saliency map, we introduce a local contrast energy feature into IPT detection framework to weight target tensor, which can efficiently suppress the background and preserve the target simultaneously. Besides, to remove the structured edges more thoroughly, we suggest an additional structured sparse regularization term using the l 1 , 1 , 2 -norm of third-order tensor. To solve the proposed model, a high-efficiency optimization way based on alternating direction method of multipliers with the fast computing of tensor singular value decomposition is designed. Finally, an adaptive threshold is utilized to extract real targets of the reconstructed target image. A series of experimental results show that the proposed method has robust detection performance and outperforms the other advanced methods. View Full-Text
Keywords: infrared image; small target detection; non-convex surrogate; singular value decomposition infrared image; small target detection; non-convex surrogate; singular value decomposition
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MDPI and ACS Style

Guan, X.; Zhang, L.; Huang, S.; Peng, Z. Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy. Remote Sens. 2020, 12, 1520. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091520

AMA Style

Guan X, Zhang L, Huang S, Peng Z. Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy. Remote Sensing. 2020; 12(9):1520. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091520

Chicago/Turabian Style

Guan, Xuewei, Landan Zhang, Suqi Huang, and Zhenming Peng. 2020. "Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy" Remote Sensing 12, no. 9: 1520. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091520

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