Spatial-Temporal Weighted and Regularized Tensor Model for Infrared Dim and Small Target Detection

Due to the confusion of target-like sparse structures and the interference of linear features in complex scenarios, many infrared small target detection methods struggle to effectively detect dim and small targets. In response to this challenge, we propose a new 3-D paradigm framework, which combines spatial-temporal weighting and regularization within a low-rank sparse tensor decomposition model. First, we design a novel spatial-temporal local prior structure tensor, named 3DST, which can significantly distinguish between targets and target-like sparse structures. Second, we introduce a three-directional log-based tensor nuclear norm (3DLogTNN) to provide a full characterization of the low-rankness of the background tensor. Third, we suggest a weighted three-directional total variation (3DTV) regularization to constrain smoothness features in background images. Finally, we develop an efficient alternating direction method of multipliers (ADMMs) to solve the proposed model. In particular, we devise a fast and accurate Sylvester tensor equation for accelerated subproblem solving. Extensive experimental results demonstrate that the proposed model has superior target detection and background suppression performance in complex scenarios compared with other detection methods.