Infrared small target detection based on hypergraph and asymmetric penalty function

Recently, infrared (IR) small target detection problem has attracted increasing attention. Component analysis-based techniques have been widely utilized, while they are faced with challenges such as low-rank background and sparse target estimation, and model construction. In this paper, an IR small target detection model with hypergraph Laplacian regularization and asymmetric penalty function-based regularization (HGLAPR) is proposed. Specifically, a spatial–temporal tensor is constructed. Then, we construct a hypergraph structure and design a hypergraph Laplacian regularization as well as a Laplace-based tensor nuclear norm for low-rank background estimation. Additionally, an asymmetric penalty function-based sparsity regularization is introduced for more accurate target estimation. To efficiently solve this model, we design an alternating direction method of multipliers (ADMM)-based optimization scheme. Extensive experiments conducted on six real IR sequences with complex scenarios illustrate the superiority of HGLAPR over ten state-of-the-art competitive methods in terms of target detectability, background suppressibility and overall performance.