Infrared Small Target Detection Based on a Group Image-Patch Tensor Model

Despite many years of research, the accuracy of small target detection is still restricted by the complex background. In addition, the contradiction between the computation complexity reduction and detection performance improvement makes it hard to apply some high-performance methods toward practical application. In this letter, a novel group image-patch tensor (GIPT) model is proposed to solve the above-mentioned problems. First, based on the structure tensor theory, a newly designed measurement criterion is employed to divide the image pixels into three types: point area, flat area, and line area. Second, image pixels with line priors and point priors are assigned to different weights to reduce the effects of strong background edges. Third, a GIPT model is proposed to better explore the low-rank of the background, in which patches with the same feature type are rearranged into the same group for tensor decomposition. Moreover, the proposed GIPT model can make the optimization process run in parallel, which can greatly reduce the complexity of the algorithms. The experiment results show the superiority of the proposed method compared with other state-of-the-art algorithms.