基于梯度方向一致性和特征分解的红外小目标检测算法

在复杂的海天背景下，现有红外小目标检测算法存在虚警率高的问题，文中深入分析目标和背景的特征差异，首先，提出了一种基于灰度差和梯度方向一致性的方法，增强了小目标并抑制了部分背景杂波，其次，结合特征分解法进一步抑制了锐利边缘背景，最后，采用自适应阈值分离出小目标。实验结果表明，与五种现有算法相比，所提出的检测算法能够在不同复杂场景都有效降低虚警率，大大提升信杂比（SCR）和背景抑制因子（BSF），并且具有良好的鲁棒性。

Infrared small target detection agorithm based on gradient direction consistency and eigendecomposition

Under the complicated sea and sky background, the existing infrared small target detection algorithms have the problem of high false alarm rate. In this paper, the feature differences between the target and the background were deeply analyzed. Firstly, a method based on gray difference and gradient direction consistency was proposed. The small target was enhanced and some background clutter was suppressed. Secondly, the sharp edge background was further suppressed by combining the eigendecomposition method. Finally, the adaptive threshold was used to separate the small target. The experimental results show that compared with the five existing algorithms, the proposed detection algorithm can effectively reduce the false alarm rate in different complex scenes, greatly improve the signal-to-clutter ratio (SCR) and the background inhibitory factor (BSF), and have good robustness.