Spatial-Temporal Tensor Ring Norm Regularization for Infrared Small Target Detection

Abstract

Infrared small target detection (ISTD) technique is widely used in infrared searching and tracking (IRST) and military surveillance. Existing detection methods must sufficiently address the challenges of the heterogeneous background with high concealment targets. In this letter, we propose a novel spatial-temporal tensor ring norm regularization (STT-TRNR) to detect infrared small targets. First, to utilize the spatial and temporal context information in a sequence, nonrepetitive spatial-temporal patches are formed by sliding windows in the consecutive frames. The patches are stacked into a tensor structure with spatial and temporal information. Second, the tensor ring nuclear norm is introduced to approximate the rank of the background tensor. The tensor ring regularization improves the correlation between dimensions, protects the internal structure of the tensor, and avoids the dimension disaster caused by train decomposition. Third, the local contrastive feature is used as a priori information to suppress the false alarms caused by the corner edges and other noises and avoid the distortion caused by target movement. Finally, the alternating direction multiplier method (ADMM) is employed to reconstruct the sequence images and retrieve the targets. The experimental results reveal that the suggested model provides improved detection performance and higher robustness across various complicated scene types.