Edge and Corner Awareness-Based Spatial–Temporal Tensor Model for Infrared Small-Target Detection

Infrared (IR) small-target detection has been a widely studied task in IR search and tracking systems. It remains a challenging problem, especially in heterogeneous scenarios, where it is very difficult to discriminate true targets from sparse residuals in the background. A novel edge and corner awareness-based spatial–temporal tensor (ECA-STT) model is presented in this article. First, we construct an STT based on a spatial–temporal correlation analysis of the IR video background. Then, we propose an indicator to highlight the target through adjustable importance measurements of the edge and corner. The tensor-based nonlocal total variation is also adopted to describe the edges in the background. The target–background separation problem is modeled as a tensor robust principal component analysis (TRPCA) problem with the tensor rank function replaced by the tensor truncated nuclear norm. The proposed model is solved by an effective optimization algorithm derived from the alternating direction method of multipliers (ADMM). Extensive experiments verify the superior abilities of the proposed model in target enhancement and background suppression.