Infrared Small Target Detection via $L_{0}$ Sparse Gradient Regularized Tensor Spectral Support Low-Rank Decomposition

Abstract

Infrared small target detection has been extensively investigated by incorporating the low-rank and sparse prior into tensor decomposition frameworks. Despite its success, the said paradigm remains several limitations in complex scenes, such as: (1) the inadequate spatial-temporal information exploitation among sequential patches; (2) the incomplete suppression of the complex background interference. To mitigate the defects, this paper provides a tensor decomposition method integrating spatial-temporal l0 sparse gradient regularization and spectral support constraint. First, we present a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">skillfully Connected Multiframe Patch Group</i> (CMPG) model to explore local spatial information and adjacent interframe correlation among multiframes patches. Then, for CMPG model, a scalable tensor spectral support constraint is employed to distinctively regularized its redundant and rare compo-nents. Considering the nonlocal uniqueness of small targets and the local continuity of rare distractors, an extended spatial-temporal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{0}$</tex-math></inline-formula> gradient constraint is embedded into target-background separation for better suppression of structural clutter, and a reweighted scheme is also used to eliminate isolated non-target points. The final model is efficiently solved by the alternating direction method of multipliers (ADMM). Experiments are conducted on extensive simulating datasets and real scenes, suggesting that the proposed method achieves a considerable boost against other competitors in terms of visual effect and subjective evaluation.