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

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.

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

“…First, the objective function is constructed according to characteristics of the tensor's background and target. Then, the objective function is solved and the detection results are obtained [23], [30], [32], [44], confirming that the combination of a tensor model and robust principal component analysis model can achieve good results. Based on small sample remote sensing data, this paper proposes a method based on small sample data [45], which uses visual features and sparse and low-rank decomposition to detect cirrus.…”

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

“…In order to eliminate the negative influence of complex backgrounds, Zhou et al [13] introduced l 1/2 -metric and dual-graph regularization in sparse component modeling. Guan et al [14] improved the IPT model using a non-convex tensor rank surrogate merging tensor nuclear norm and the Laplace function. This type of method performs well when the background satisfies the large spatial spread assumption but they cannot handle cluttered backgrounds satisfactorily.…”

Infrared small target detection based on non‐convex triple tensor factorisation

“…In addition to this, RIPT model [17] treats singular values equally, so, less weight should be allocated to the larger singular values. Also to lower down the burden of SVD computation, and to approximate the low rank background tensor patch properly, tensor nuclear norm (TNN) [23] has been applied recently in many infrared small target detection approaches [18,24,25,19,20]. Hence, our first motivation is to propose a method which could address the low-rank background tensor approximation, as well as, the SVD computational cost issue.…”