Spatial-Temporal Tensor Representation Learning With Priors for Infrared Small Target Detection

The poor quality images with scarce target characteristics and complicated textures make it tough to enhance target detectability (TD) and background suppressibility (BS) simultaneously in infrared (IR) small target detection. Representation learning has gradually shown superiority, but two key issues arise. The first is constructing an appropriate dictionary. The second is a comprehensive and accurate representation learning model. This article introduces a low-rank and sparse spatial-temporal tensor representation learning model based on local binary contrast measure (STRL-LBCM), which transforms the target detection into an optimization task for representation and decomposition of background, target, and noise components from spatial-temporal aspects. First, we construct an IR spatial-temporal tensor from the original sequence, followed by improving GoDec plus to obtain a sparse part and a background dictionary, which lays the foundation for representation learning model. Then, a rank surrogate named fully connected tensor nuclear norm (FCTNN) is proposed for low-rank coefficient, aiming at low-rank background estimation. Meanwhile, with the sparse part, we design a salient prior called local binary contrast measure, which extracts more effective prior information for guiding target detection. Furthermore, not only Frobenius norm but also a 3-D weight total variation regularization is utilized to preserve background details and remove noise. Finally, an efficient solving framework is designed for the proposed model. Comprehensive experiments reveal that STRL-LBCM provides superior TD, BS, and overall performance compared with 16 target detection methods.