Abstract
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.
Highlights
Infrared imaging as an important means of photoelectric detection has wide applications, such as space surveillance, remote sensing, missile tracking, infrared search and track (IRST), etc. [1,2,3,4]
To overcome these problems of reweighted infrared patch-tensor (RIPT) and partial sum of tensor nuclear norm (PSTNN), we introduce a local contrast energy feature into infrared patch-tensor (IPT) detection framework in this paper
RIPT and PSTNN have achieved the best results as the proposed method in most cases, but it can be observed that RIPT would shrink the target pixels to 0 for the 18th frame of Sequence 2, resulting in the target not being detected, and that PSTNN compared with the proposed method is slightly worse for Sequence 6, which contains extremely complex ground background
Summary
Infrared imaging as an important means of photoelectric detection has wide applications, such as space surveillance, remote sensing, missile tracking, infrared search and track (IRST), etc. [1,2,3,4]. Due to complex backgrounds and heavy imaging noise, infrared images usually have low signal-to-clutter ratio, making target detection exceedingly difficult [8,9,10]. Various interferences, such as heavy cloud edges, sea clutters, and artificial heat source on the ground, usually cause high false alarm rates and weaken detection performance. When the relative motion between infrared imaging device and the target is slow and the background is uniform, consistent information of adjacent frames can be obtained and sequence detection methods improve the performance. It is necessary to merge the two methods based on different prior information into a detection framework
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