  Infrared small target detection is an important fundamental task in the
infrared system. Therefore, many infrared small target detection methods have
been proposed, in which the low-rank model has been used as a powerful tool.
However, most low-rank-based methods assign the same weights for different
singular values, which will lead to inaccurate background estimation.
Considering that different singular values have different importance and should
be treated discriminatively, in this paper, we propose a non-convex tensor
low-rank approximation (NTLA) method for infrared small target detection. In
our method, NTLA regularization adaptively assigns different weights to
different singular values for accurate background estimation. Based on the
proposed NTLA, we propose asymmetric spatial-temporal total variation (ASTTV)
regularization to achieve more accurate background estimation in complex
scenes. Compared with the traditional total variation approach, ASTTV exploits
different smoothness intensities for spatial and temporal regularization. We
design an efficient algorithm to find the optimal solution of our method.
Compared with some state-of-the-art methods, the proposed method achieves an
improvement in terms of various evaluation metrics. Extensive experimental
results in various complex scenes demonstrate that our method has strong
robustness and low false-alarm rate. Code is available at
https://github.com/LiuTing20a/ASTTV-NTLA.
