Gaussian Scale-Space Enhanced Local Contrast Measure for Small Infrared Target Detection

Abstract

Robust small-target detection plays an important role in the infrared (IR) search and track system, but it is still a challenge to detect small IR target under complex background. In this letter, an effective method inspired by the scale-space theory and the contrast mechanism of the human vision system is proposed. First, Gaussian scale-space (GSS) of an IR image is constructed by the convolution of a variable-scale Gaussian function. Second, the gray features of the local image can be directly represented by downsampling in a scale image, and enhanced local contrast measure (ELCM) is defined to enhance small target and suppress complex background. Then, the saliency map is obtained by using max-pooling operation, and an adaptive threshold is adapted to segment real targets. Experimental results on a test set with three real IR sequences demonstrate that the proposed method has a good performance in target enhancement and background suppression, and shows strong robustness under complex background. Especially, the proposed method has high computational efficiency, which can improve detection speed.