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 paper, 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 (ADMM). We unroll the iterative algorithm into deep neural networks, where a generalized sparsifying transform and a singular value thresholding operator are learned by the convolutional neural networks (CNNs) to avoid tedious parameter tuning and improve the interpretability of the neural networks. We conduct compre- hensive 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.