Infrared small target detection plays an important role in the remote sensing
fields. Therefore, many detection algorithms have been proposed, in which the
infrared patch-tensor (IPT) model has become a mainstream tool due to its
excellent performance. However, most IPT-based methods face great challenges,
such as inaccurate measure of the tensor low-rankness and poor robustness to
complex scenes, which will leadto poor detection performance. In order to solve
these problems, this paper proposes a novel double-weighted multi-granularity
infrared patch tensor (DWMGIPT) model. First, to capture different granularity
information of tensor from multiple modes, a multi-granularity infrared patch
tensor (MGIPT) model is constructed by collecting nonoverlapping patches and
tensor augmentation based on the tensor train (TT) decomposition. Second, to
explore the latent structure of tensor more efficiently, we utilize the
auto-weighted mechanism to balance the importance of information at different
granularity. Then, the steering kernel (SK) is employed to extract local
structure prior, which suppresses background interference such as strong edges
and noise. Finally, an efficient optimization algorithm based on the
alternating direction method of multipliers (ADMM) is presented to solve the
model. Extensive experiments in various challenging scenes show that the
proposed algorithm is robust to noise and different scenes. Compared with the
other eight state-of-the-art methods, different evaluation metrics demonstrate
that our method achieves better detection performance in various complex
scenes.

该研究提出了一种新的双加权多层次红外图像小目标检测模型，通过采集不重叠的图像块和使用张量分解来构建多层次红外图像小目标模型，利用自适应加权机制平衡不同粒度信息的重要性，并利用导向核提取局部结构信息，最终通过交替方向乘子法进行模型优化，实验结果表明该方法在不同场景下具有很好的鲁棒性和检测性能。