Sparse Regularization-Based Spatial–Temporal Twist Tensor Model for Infrared Small Target Detection

Infrared (IR) small target detection under complex environments is an essential part of IR search and track systems. However, previously proposed IR small target detection algorithms cannot achieve complete suppression of complex and significant backgrounds. The spatial–temporal information of image sequences is not fully exploited. In this article, we present a sparse regularization-based twist tensor model for IR small target detection. First, the twist tensor model is built via perspective conversion based on the target’s local continuity in the spatial–temporal domain, which makes the original complicated background components more structured and increases the difference between the background and the target. Then, the structured sparsity-inducing norm is introduced to define the locality and continuity of the target. To further minimize the sparse background structures and global noise, the structured sparsity-inducing norm and the
$l_{1}$
norm are combined as the target’s parse constraint. Experimental results on real scenes reveal that the suggested method can process images with high detection accuracy and outstanding background suppression ability compared to various state-of-the-art methods.