Small infrared target detection based on low-rank and sparse representation

Abstract

The method by which to obtain the correct detection result for infrared small targets is an important and challenging issue in infrared applications. In this paper, a low-rank and sparse representation (LRSR) model is proposed. This model can describe the specific structure of noise data effectively by utilizing sparse representation theory on the basis of low-rank matrix representation. In addition, LRSR based infrared small target detection algorithm is presented. First, a two-dimensional Gaussian model is used to produce the atoms that construct over-complete target dictionary. Then, the reset image data matrix is decomposed by the LRSR model to obtain the background, noise and target components of the image. Finally, the target position can be determined by threshold processing for the target component data. The experimental results in single objective frame, multi-objective image sequences, and strong noise background conditions demonstrate that the proposed method not only has high detection performance in effectively reducing the false alarm rate but also has strong robustness against noise interference.