Infrared small target detection based on local-image construction and maximum correntropy

Infrared small target detection has been widely applied in the civil and military fields, but is hard to deal with complex background and highlight edge. To achieve fast and robust target detection, a novel strategy is proposed in this paper. Initially, the local-image method is used to construct a single-frame infrared image into a low-rank sparse matrix with close spatial relevance. Then, the sparse matrix based on maximum correntropy is used to express the small target, which is applied to the improved bilateral random projection method to achieve more robust and faster decomposition for small target. Finally, in order to achieve better convergence result, the prominent edge structure tensor for infrared image is designed and separated from the sparse matrix of target. Extensive experimental results show that the proposed algorithm is obviously superior to other methods in running time and detection rate, especially for highlight cluster.