点击次数：

所属单位：电子信息工程学院

发表刊物：IEEE Access

摘要：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 l1 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. © 2013 IEEE.

是否译文：否

发表时间：2019-01-01

合写作者：周飞,戴一冕,王鹏,倪康

通讯作者：吴一全