基于改进随机漫步者非凸秩逼近最小化的复杂背景下红外点目标检测方法研究

The infrared point target detection algorithm based on modified random walker and non-convex rank approximation minimization under the complex background

红外点目标检测是红外制导系统的关键技术之一,是军事应用领域的研究热点。一方面,点目标在大气传输和散射过程中由于观测距离长,常常淹没在背景杂波和大噪声中,信噪比低。另一方面,图像中的目标以模糊点的形式出现,使得目标没有明显的特征和纹理信息。由于不同红外图像中的点目标具有不同的外观、形状和姿态,加之噪声杂波的干扰遮挡,经过单帧检测后,除了真实目标外,图像中可能有虚假目标和一些强噪声。因此,由于这几个因素,红外点目标检测变得非常困难。为了解决这一问题,作者研究了红外点目标检测的相关方法,提出了非凸秩逼近最小化方法(NRAM)与改进的随机漫步者方法(MRW)相结合的方法(NRAM-MRW),在复杂的红外背景下,针对红外点目标的检测中有着较好的检测效果。

Infrared point target detection is one of the key technologies of the infrared guidance system.On the one hand,due to the long observation distance,the point target is often submerged in the background clutter and large noise in the process of atmospheric transmission and scattering,and the signal-to-noise ratio is low.On the other hand,the target in the image appears in the form of fuzzy points,so that the target has no obvious features and texture information.Therefore,due to these two factors,infrared point target detection becomes intensely difficult.In order to address the issue,the relevant algorithms of point infrared target detection are studied,and a combination algorithm of non-convex rank approximation minimization algorithm and the modified random walker algorithm(NRAM-MRW)is proposed,which has a better detection effect of point infrared target detection under complex background.