梯度差各向异性高斯滤波的红外小目标检测

Infrared Small Target Detection Using Gradient Differential Anisotropic Gaussian Filtering

针对现存背景抑制算法未能有效抑制复杂背景而导致的高虚警率及低检测率问题,提出一种基于六方向梯度差各向异性高斯滤波抑制、双层正交灰度差与对角灰度差目标增强、灰度指数自适应阈值分割的小目标检测算法。首先,采用高斯滤波技术融入梯度差思想设置一系列背景抑制策略;接着,将抑制后的图像利用正交灰度差与对角灰度差映射在双层滑窗上增强局部对比度,提高目标显著性;最后,通过像素灰度指数自适应分割算法检测真实弱小目标。实验结果表明,该算法的背景抑制因子指标高达93%,能随背景局部变化来自适应建立背景抑制模型,从而自适应抑制复杂背景突出目标。

To resolve the problem of high false alarm rate and low detection rate caused by the failure of existing background suppression algorithms in effectively suppressing complex backgrounds,a small target detection algorithm based on six-direction gradient difference anisotropic Gaussian filter suppression,double-layer orthogonal gray difference and diagonal gray difference target enhancement,and gray index adaptive threshold segmentation is proposed herein.First,a series of background suppression strategies are created using the Gaussian filtering technology and gradient difference concept.Then,the suppressed image is mapped on a double-layer sliding window using orthogonal gray difference and diagonal gray difference to enhance its local contrast as well as improve its target saliency.Finally,the real weak target is detected using the adaptive segmentation algorithm of the pixel gray index.Experimental results show that the background suppression factor index of the algorithm increases to 93%,and it can modify a background suppression model based on the local changes in the background to adaptively suppress prominent targets in complex backgrounds.