Infrared small target detection via self-regularized weighted sparse model

Infrared search and track (IRST) system is widely used in many fields, however, it's still a challenging task to detect infrared small targets in complex background. This paper proposed a novel detection method called self-regularized weighted sparse (SRWS) model. The algorithm is designed for the hypothesis that data may come from multi-subspaces. And the overlapping edge information (OEI), which can detect the background structure information, is applied to constrain the sparse item and enhance the accuracy. Furthermore, the self-regularization item is applied to mine the potential information in background, and extract clutter from multi-subspaces. Therefore, the infrared small target detection problem is transformed into an optimization problem. By combining the optimization function with alternating direction method of multipliers (ADMM), we explained the solution method of SRWS and optimized its iterative convergence condition. A series of experimental results show that the proposed method outperforms state-of-the-art baselines. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The paper proposed a novel detection method called SRWS model for detecting infrared small targets in complex backgrounds. By utilizing the hypothesis of multi-subspaces, overlapping edge information, and self-regularization item, the method transforms the detection problem into an optimization problem and utilizes ADMM for solution. Experimental results demonstrate that the proposed method outperforms state-of-the-art baselines.