A Difference-Based Local Contrast Method for Infrared Small Target Detection under Complex Background

Abstract

Infrared small target detection under complex background is of great significance in the field of remote sensing, such as optical remote sensing, infrared precise guidance, infrared surveillance, and night navigation. Because of low-contrast and complex-background clutters of infrared images, infrared small target detection under complex background is difficult and has a serious false alarm. In this paper, a difference-based local contrast method is proposed to improve the detection performance. First, a median filtering process is used to reduce pixel-sized noises, and infrared image after filtering is divided into a series of sub-images. Then, a difference-based local contrast measure based on contrast mechanism is calculated for generating the saliency map, which can exceedingly improve the detection rate and reduce the false alarm rate. Eventually, an adaptive threshold is used to extract the target sub-image. Experiments on five real infrared small target image datasets show that the proposed method is robust and effective with great respect to detection accuracy. Our method can achieve a higher detection rate and lower false alarm rate under complex background with better performance in target enhancement and background suppression compared with the traditional algorithms.