Infrared Small Target Detection Based on Four-Direction Overlapping Group Sparse Total Variation

Received: 12 January 2020 Accepted: 23 April 2020 This paper aims to develop an efficient, robust and reliable infrared detection method for small targets. Firstly, the data structure of infrared images with small targets was analyzed in details. Then, the infrared small target detection was converted into the decomposition of the robust principal component analysis (RPCA), and the objective function was constructed with the idea of variation. Next, a regularization term called four-direction overlapping group sparse total variation (OGSTV) was created, and a TV4OGS-RPCA model was designed for infrared small target detection. Experimental results prove that our model can effectively separate small targets from the background, and accurately pinpoint the small targets in infrared images.