Infrared dim and small target detection based on total variation and multiple noise constraints modeling

Abstract

To improve the ability of infrared dim small target detection algorithm based on traditional infrared patch-image (IPI) model, a new detection model based on total variation and multiple noise constraints is proposed. We firstly transform the original infrared image into an IPI, and then the total variational regularization constrains the background patch-image in order to reduce the noise on the target image. In the meantime, the edge information of the image can be preserved to avoid excessive smoothness of the restored background image. Additionally, considering the lack of noise distribution in the patch-image, the combined and norm are introduced to describe the noise more accurately. The experimental results show that the proposed method can suppress the background clutter better and improve detection performance effectively.