Infrared Small Target Detection Algorithm Based on Local Spatial Gradient Peak

Abstract A sparse infrared small target detection algorithm based on local spatial gradient peaks is proposed to deal with the problem of slow running speed and edge sensitivity in low-rank decomposition methods.The detection steps are as follows. In the first step, the image expansion operation is used for preprocessing. We use the circular structure element to sharpen the edges of targets and smooth the background noise. Then, the saliency gradient features of the target local region are applied to calculate the overlapping gradient information of the image after expansion. The local area with a larger gradient peak is located in the original image, and the selected local area is considered to be the region of interest with candidate targets. Finally, we use the advanced accelerated proximal gradient algorithm to perform matrix decomposition in the extracted local regions of interest to extract sparse infrared small targets. Extensive experimental results under real scenarios illustrated that compared with the baseline low-rank sparse decomposition method, the proposed approach runs faster and shows superior detection performance in the comprehensive evaluation index.