各向异性SUSAN滤波红外弱小目标检测  

Infrared dim target detection based on anisotropic SUSAN filtering

作　　者：景亮[1] 彭真明[1] 何艳敏[1] 蒲恬[1] 

机构地区：[1]电子科技大学光电信息学院,成都610054

出　　处：《强激光与粒子束》2013年第9期2208-2212,共5页High Power Laser and Particle Beams

基　　金：中央高校基本科研业务费专项资金项目(ZYGX2010J063);中国科学院光束控制重点实验室基金项目(2010LBC001)

摘　　要：为了解决SUSAN滤波算子不能自适应调整滤波系数的问题,采用Geusebroek提出的各向异性高斯滤波器替代SUSAN滤波算子中的高斯滤波部分。由局部图像的方差和像素的邻域平滑度决定长短轴的方差,由该点的梯度方向决定滤波器的长轴方向,由局部图像的灰度值与均值差的一阶范数确定SUSAN滤波器的阈值,从而构造出各向异性SUSAN滤波器。将其用于红外弱小目标检测中,实验结果表明:各向异性SUSAN滤波器能够很好地保留图像中的边缘信息,使残差图像中弱小目标的信噪比增益和信杂比增益极大地提高,目标大小得到较好的保留,虚警率下降。The SUSAN filtering operator can not self-adaptively modulate filter coefficients due to the use of an axisymmetric Gaussian filter.In order to solve this problem,this article presents an anisotropic SUSAN filter using an anisotropic Gaussian filter instead of a Gaussian filter in the SUSAN filtering operator.The variances of long axis and short axis are determined by the variance and the pixel neighborhood smoothness of the partial image.The long axis direction is determined by the gradient direction of the point.The SUSAN filter threshold is determined by the first order norm of the difference of the gray value and the mean value.For the infrared dim target detection,experimental results show that the anisotropic SUSAN filter could preserve the image edge information,the signal-to-noise ratio gain and signal-to-clutter ratio gain greatly improved in the residual image,the size of the target got a better retention,and the false alarm decreased.

关 键 词：SUSAN滤波器 各向异性滤波 背景建模 红外图像 弱小目标检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]