A Simplified Dual-Weighted Three-Layer Window Local Contrast Method for Infrared Small-Target Detection

Abstract

In the realm of infrared small-target detection, the weighted local contrast approaches, which seek to improve targets by the defined weighted factors, have garnered a lot of interest. However, there are several problems with these methods as follows. 1) The vast number of local contrast sliding sub-windows restricts the time efficiency. 2) The dim targets in the complicated backgrounds are incorrectly eliminated by the background suppression procedure. 3) The background noise in the complicated environment cannot be effectively muted. A simplified dual-weighted three-layer window local contrast method (SDWTLLCM) is suggested in this work as a solution to these issues. In order to extract tiny targets and suppress complicated backgrounds, a hierarchical convolution filtering window is first created. Then, even without sub-window division, a simple three-layer sliding window is created for time efficiency enhancement. The dual-weighted local contrast approach is also intended to minimize the background and further highlight tiny objects. Eventually, the tiny targets may be extracted more effectively using the adaptive threshold segmentation procedure. The vast experimental findings show that our suggested strategy is effective and efficient.