Infrared small target detection using the global low-rank and local smoothness coupled representation with local structure

Abstract

Infrared small target detection is crucial in both military and civilian applications. However, existing low-rank and sparse decomposition (LRSD) methods often suffer from noise residues caused by inaccurate background estimation. This is because the low-rank and smoothness of the background exhibit inherent coupling properties. It is usually difficult to accurately fit complicated background using a single regularization term or their additive hybrid model. This paper tackles this issue by proposing a coupled tensor model that incorporates global low-rank and local smoothness. Furthermore, to suppress potential “grid artifacts”, which are usually brought on by the infrared device’s pixel array characteristics and total variation, another regularization term that focuses on the minimum absolute structure of the tensor’s gradient in the local region is constructed. The proposed model is then solved using an optimization framework based on the alternating direction method of multipliers (ADMM). Finally, comparative experiments on three public datasets demonstrate that the proposed model outperforms existing state-of-the-art LRSD methods in terms of suppressing complicated background and sparse “grid artifacts”.