An Efficient Algorithm for Infrared Small Target Detection

An improved efficient fractal algorithm, based on higher-order statistics (HOS), is presented for infrared (IR) small target detection under complex background of a single image. This algorithm is divided into two parts: coarse location and fine location. It firstly uses higher-order statistics to locate the target coarsely, and then a region of interest (ROI) containing the infrared small target is obtained. Subsequently, a fractal dimension image of the ROI is constructed based on the fractal theory. At last, self-adaptive threshold segmentation is applied to the fractal dimension image to get the exact detection result. The experimental results show that compared with the traditional fractal method, the proposed algorithm is more effective and faster for infrared small target detection.