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-k₁k₂ extension tensor tubal rank (METTR) is defined, the elements of which include the tubal ranks of all tensors expanded via mode-k₁k₂ extension. Through the mode-k₁k₂ 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 l₁ 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.

复杂海洋环境下的红外暗光目标检测在军事和民用领域具有关键作用。许多最先进的方法都存在泛化能力低、对噪声和顽固边缘的鲁棒性差、时间复杂度高、检测结果中存在背景残差或目标缺陷等缺点。为了进一步解决这些不足，本文基于红外贴片张量（IPT）模型，提出了一种鲁棒的红外暗淡目标检测算法，将目标检测任务转化为凸优化问题。针对目前IPT模型中张量秩逼近问题还没有很好解决的情况，提出了一种新的张量秩向量形式，命名为mode- k ₁ k ₂定义了扩展张量 tubal rank (METTR)，其元素包括通过 mode- k ₁ k ₂扩展扩展的所有张量的 tubal rank 。通过张量的mode- k ₁ k ₂扩展，可以更好地挖掘张量不同模式之间的隐藏信息。为了有效地最小化 METTR，我们提出了它的凸逼近范数 METTR，并建立了张量鲁棒主成分分析（TRPCA）模型联合l ₁规范。然后我们使用交替方向乘法器方法（ADMM）并设置最优参数来求解所提出的模型。一系列实验结果表明，该算法在背景抑制和目标检测方面优于基线。