Infrared dim target detection based on total variation regularization and principal component pursuit

Robust detection of infrared dim and small target contributes significantly to the infrared systems in many applications. Due to the diversity of background scene and unique characteristic of target, the detection of infrared targets remains a challenging problem. In this paper, a novel approach based on total variation regularization and principal component pursuit (TV-PCP) is presented to deal with this problem. The principal component pursuit model only considers the low-rank feature of background images, which will result in poor detection ability in non-uniform and non-smooth scenes. We take into account the total variation regularization term to thoroughly describe background feature, which can achieve good detection result as well as good background estimation result. Firstly, the input infrared image is transformed to a patch image model. Secondly, the TV-PCP model is presented on the patch image. An effective optimization algorithm is proposed to solve this model. Experiments on six real datasets show that the proposed method has superior detection ability under various backgrounds, especially with good background suppression performance and low false alarm rate. (C) 2017 Elsevier B.V. All rights reserved.