Infrared small target detection based on local intensity and gradient properties

Infrared small target detection is a challenging task for computer vision due to the factors such as scale variations of the targets and strong clutters. Inspired by the Gaussian-like shape of the small target, we characterize two local properties of the small target from the perspective of intensity and gradient to address this problem. The two properties are that the intensity value of the target pixels is greater than the value of its locally neighboring pixels, and the gradients towards the target center often distribute regularly around the target. First, based on the two properties, the local intensity and gradient (LIG) map is calculated from the original infrared image in order to enhance the targets and suppress clutters. Next, we can obtain the targets conveniently via segmentation from the LIG map. Extensive evaluations on real data demonstrate that the proposed algorithm has satisfactory results in terms of clutter suppression and robustness.