Infrared Small Target Detection With Patch Tensor Collaborative Sparse and Total Variation Constraint

Abstract

Sparse and low-rank modeling has shown a powerful describing abilities to express small target, however, low-rank model exists the problem of insufficient rank approximation deviation ability and excessive shrinkage, which will lead to inaccurate background estimation. In this letter, a new non-convex approximation function using Gaussian model is built toward deeply excavating low-rank information of background as much as possible. In the sparse collaborative representation, the local prior confidence (LPC) is integrated into the target structure tensor to maximum to distinguish target region and background edge more accurately. In the process of background restoration, total variation constraint (TVC) is employed apropos of describing gray variation of small target in complex background more precisely and improving accuracy of background restoration to further perfectly recover small targets. The low-rank and sparse recovery algorithm engages alternating direction multiplier method (ADMM) for iterative calculation and solution. Compared with advanced optimal algorithms, a great number of experimental results show that the proposed model improves the adaptability and robustness of the detection algorithm to variety of complex scenes, and has lasting vitality and high application value.