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

Infrared Small Target Detection Using Double-Weighted Multi-Granularity Patch Tensor Model With Tensor-Train Decomposition