Deep Low-Rank and Sparse Patch-Image Network for Infrared Dim and Small Target Detection

Detection of infrared dim and small targets with diverse and cluttered background plays a significant role in many applications. In this article, we propose a deep low-rank and sparse patch-image network, termed as Deep-LSP-Net, to effectively detect small targets in a single infrared image. Specifically, by using the local patch construction scheme, we first transform the original infrared image into a patch image, which can be decomposed as a superposition of the low-rank background component and the sparse target component. The target detection is thus formulated as an optimization problem with low-rank and sparse regularizations, which can be solved by the alternating direction method of multipliers (ADMMs). We unroll the iterative algorithm into deep neural networks, where a generalized sparsifying transform and a singular value thresholding (SVT) operator are learned by the convolutional neural networks (CNNs) to avoid tedious parameter tuning and improve the interpretability of the neural networks. We conduct comprehensive experiments on two public datasets. Both qualitative and quantitative experimental results demonstrate that the proposed algorithm can obtain improved performance in small infrared target detection compared with state-of-the-art algorithms.