Infrared Small Target Detection Via Learned Infrared Patch-Image Convolutional Network

Xinyu Zhou, Ye Zhang, Yue Hu

Abstract: Small infrared target detection is a significant technique in both civil and military applications. Regularized optimization methods that exploit both the sparsity and the low-rank prop-erties of the infrared image have achieved good performance. In this paper, we propose to unroll the sparse and low-rank regularized model to a deep neural network to effectively sep-arate the infrared target and the background. Specifically, we adopt the infrared patch-image (IPI) model to transform the original infrared image into a patch-image using local patch construction. A deep network flow graph is proposed by si-multaneously exploiting a learned low-rank prior and a spar-sity prior to promote the target detection performance. Exper-imental results demonstrate that the proposed IPI-net is able to provide improved performance in small infrared target de-tection compared with the state-of-the-art algorithms.