Infrared dim target detection via mode-k1k2 extension tensor tubal rank under complex ocean environment

Infrared dim target detection under complex ocean environment plays a key role in military and civilian fields. Many state-of-the-art methods have disadvantages such as low generalization ability, poor robustness to noise and stubborn edges, high time complexity, and the existence of background residuals or target defects in detection results. To further solve these shortcomings, based on the infrared patch tensor (IPT) model, a robust infrared dim target detection algorithm is proposed in this paper, which converts the target detection task into a convex optimization problem. Aiming at the current situation that the tensor rank approximation in the IPT model has not been well resolved, a new vector form of tensor rank named mode-k(1)k(2) extension tensor tubal rank (METTR) is defined, the elements of which include the tubal ranks of all tensors expanded via mode-k(1)k(2) extension. Through the mode-k(1)k(2) extension of the tensor, the hidden information among the different modes of the tensor is better mined. To minimize the METTR efficiently, we propose its convex approximation norm METTR, and establish a tensor robust principal component analysis (TRPCA) model joint l(1) norm. Then we use the alternating direction multiplier method (ADMM) and set the optimal parameters to solve the proposed model. A series of experimental results show that the proposed algorithm outperforms the baselines in terms of background suppression and target detection.

Automated summary

A robust infrared dim target detection algorithm based on the infrared patch tensor model is proposed in this paper, which effectively addresses the shortcomings of traditional methods through a new tensor rank representation and convex optimization approach. Experimental results demonstrate its superiority in background suppression and target detection.