Graph Laplacian regularization for fast infrared small target detection

Ting Liu, Yongxian Liu, Jungang Yang, Boyang Li, Yingqian Wang, Wei An

Abstract: Highlights•Different from usual graphs that are constructed on each pixel or patch of infrared image, we propose to construct the graph with each frame of infrared sequence.•The proposed graph construction approach can reduce the number of vertices in the graph, thereby decreasing computational complexity.•Based on the above graph construction, we propose a fast graph Laplacian regularization (FGLR). The proposed FGLR can describe the low-rank information between infrared sequence images.•Compared with nuclear norm, the proposed FGLR avoids singular value decomposition computation and has a closed-form solution for background estimation. Therefore, the proposed FGLR is more efficient than nuclear norm.•We use Minimax Concave penalty function instead of l1 norm to describe the sparsity of targets.•The proposed method is solved by alternating direction multiplier method.

External IDs:dblp:journals/pr/LiuLYLWA25