Infrared small target detection using kernel low-rank approximation and regularization terms for constraints

Abstract

The robust detection of infrared (IR) small targets remains a challenging task. When targets are submerged in heavy clutter or disturbed by strong edge, it is difficult to extract targets and suppress background clutter simultaneously. In this paper, we present a kernel robust principal component analysis model for IR small target detection. Firstly, to overcome the interference of heavy clutter and strong edge, the kernel low-rank approximation is proposed to estimate the background component; Then, to further improve detection performance, the lp-norm is adopted to recover the sparse component, the total variation regularization is employed to smooth the background, and the graph Laplacian regularization is utilized as a sparse constraint to constrain the homogeneous target region. Finally, we derive an effective method to solve the proposed model based on the alternating direction method of multipliers. Experimental results not only show the proposed method can robustly and effectively detect IR small targets even when the targets are submerged in strong clutter or seriously disturbed by strong edge, but also confirm the proposed method is competitive with the state-of-the-art methods.