LAUSR

Against complex background containing the tiny target, high-performance infrared small target detection is always treated as a difficult task. Many low-rank recovery-based methods have shown great potential, but they may suffer from high false or missing alarm when encountering the background with intricate interferences. In this paper, a novel graph-regularized Laplace low-rank approximation detecting model (GRLA) is developed for infrared dim target scenes. Initially, a non-convex Laplace low-rank regularizer instead of the nuclear norm is employed to boost the accuracy of heterogeneous background estimation. Then, to maintain the intrinsic structure between background patch-image, the graph regularization is incorporated in the detecting model. Besides, aiming at reducing the nontarget outliers, a reweighted $l_{1}$ norm with nonnegative constraint is used. Finally, the proposed model is extended to a generalized framework (G-GRLA) by replacing different non-convex rank functions. With the help of the alternating direction method of multiplier (ADMM), the solution of the proposed model is obtained by an iterative optimization scheme. The experimental results on extensive actual infrared images present the superior performance of our proposed method to compare with the state-of-the-art methods.