Infrared Small Target Detection Based on Target-and-Background-Weighted Two Layer Nested Model

Infrared (IR) small target detection is a critical technology in both military and civil applications. Nowadays, the optimization-based methods are widely concerned for their good performance under complex backgrounds. However, these optimization models are constructed simply, without sufficient information to fully characterize the features of an image. Moreover, the true target is unprotected in the target weight, and appropriate structural constraints on background are also deficient. Consequently, an IR small target detection algorithm based on target-and-background-weighted two-layer nested model is proposed in this letter. Initially, a two-layer nested model is designed to characterize the global and the local structure of an IR image simultaneously. Then, the smallest eigenvalue of structure tensor (SEoST) is introduced to reweight the overlapping edge information (OEI) map as a target weight, in order to reduce the false alarm rate without missed detection. Furthermore, a weight matrix of background is elaborated as a structural constraint, in order to get a more accurate background estimation result. Finally, the alternating direction multiplier method (ADMM) with a soft-threshold solver is designed to solve the proposed optimization model. Experiments are carried out to evaluate the proposed method by comparing with eight baseline methods, and the results show that the proposed method achieves superior detection performance under complex scenes.