Infrared small target detection plays an important role in many infrared systems. Recently, many infrared small target detection methods have been proposed, in which the lowrank 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 adaptively assigns different weights to different singular values for accurate background estimation. Based on the proposed NTLA, we use the asymmetric spatial-temporal total variation (ASTTV) to thoroughly describe background feature, which can achieve good background estimation and detection in complex scenes. Compared with the traditional total variation approach, ASTTV exploits different smoothness strength for spatial and temporal regularization. We develop an efficient algorithm to find the optimal solution of the proposed model. Compared with some state-of-the-art methods, the proposed method achieve an improvement in different evaluation metrics. Extensive experiments on both synthetic and real data demonstrate the proposed method provide a more robust detection in complex situations with low false rates.
翻译:红外线小目标探测在许多红外系统中起着重要作用。最近,提出了许多红外小目标探测方法。最近,提出了许多红外小目标探测方法,其中使用低级别模型作为强有力的工具。然而,大多数低级别方法对不同的单数值赋予相同的权重,从而导致背景估计不准确。考虑到不同的单值具有不同的重要性,因此在本文中应当区别对待,我们提议对红外小目标探测采用一种非convex shor低级别近似(NTLA)法。在我们的方法中,NTLA适应性地为准确的背景估计对不同的单值分配不同的权重。根据拟议的NTLA,我们使用不对称的空间-时空总变异(ATTV)来彻底描述背景特征,这些特征可以在复杂的场景中实现良好的背景估计和检测。与传统的全变法方法相比,ASTTV利用不同的平滑度强度来调节空间和时间。我们开发一种高效的算法,以找到最佳的解决方案。与一些最新方法相比,拟议方法在不同的合成测算法中实现了一种较强的精确的测测测度。在不同的测测度上提供了一种较强的精确的测算方法。