Infrared Small Target Detection Based on Partial Sum Minimization and Total Variation

In the advanced applications, based on infrared detection systems, the precise detection of small targets has become a tough work today. This becomes even more difficult when the background is highly dense in addition to the nature of small targets. The problem raised above is solved in various ways, including infrared patch image (IPI) based methods which are considered to have the best performance. In addition, the greater shrinkage of singular values in the methods based on IPI leads to the problem of nuclear norm minimization (NNM), which leads to the problem of incorrectly recognizing small targets in a highly complex background. Hence, this paper proposed a new method for infrared small target detection (ISTD) via total variation and partial sum minimization (TV-PSMSV). The proposed TV-PSMVS in this work basically replaces the IPI's NNM with partial sum minimization (PSM) of singular values and, additionally, the total variance (TV) regularization term is inducted to the background patch image (BPI) to suppress the complex background and enhance the target object of interest. The mathematical solution of the proposed TV-PSMSV approach was performed using alternating direction multiplier (ADMM) to verify the proposed solution. The experimental evaluation using real and synthetic data set was performed, and the result revealed that the proposed TV-PSMSV outperformed existing referenced methods in the terms of background suppression factor (BSF) and the signal to gain ratio (SCRG).

Automated summary

This study proposes a new method for infrared small target detection using total variation and partial sum minimization. The method enhances the target object of interest by controlling the variance of the background image and minimizing the partial sum of its singular values. Experimental results show that the proposed method outperforms existing methods in terms of background suppression and signal gain.