Infrared Small Target Detection Based on Weighted Tensor Average Rank Minimization and Directional Structure Tensor

Xi-Hu Yang, Yu-Bang Zheng, Jia-Jie Yin, Shi-Jun Yang, Heng-Chao Li

Abstract: To address the problems of poor robustness and target over-shrinkage in complex backgrounds of infrared small target detection (ISTD) algorithms, we propose a novel model by two steps. Firstly, we introduce a weighted tensor average nuclear norm with lp function (WTANN-lp) as the constraint term of the infrared background patch tensor to eliminate the issue of information’s underutilization due to transposition variability. This improves the robustness of the algorithm under complex background. Secondly, we design a directional structure tensor (DST) for extracting the target’s local prior information, which can distinguish the target from sparse residuals in multiple directions and overcome the challenge of target over-shrinkage. We use the alternating direction multiplier method (ADMM) to solve the proposed model, and extensive experiments demonstrate the superior target detection and shape reproduction performance of the proposed model compared to seven baseline detection methods.