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兵工学报 ›› 2015, Vol. 36 ›› Issue (4): 687-695.doi: 10.3969/j.issn.1000-1093.2015.04.017

• 论文 • 上一篇    下一篇

基于复无下采样轮廓波和Gaussian小波支持向量回归的红外目标图像背景抑制

吴一全1,2,3, 宋昱1   

  1. (1.南京航空航天大学 电子信息工程学院江苏 南京 210016; 2.光电控制技术重点实验室河南 洛阳 471009;
  • 收稿日期:2013-10-16 修回日期:2013-10-16 上线日期:2015-06-02
  • 作者简介:吴一全(1963—),男,教授,博士生导师
  • 基金资助:
    国家自然科学基金项目(60872065);光电控制技术重点实验室和航空科学基金项目(20105152026);中航工业合作创新产学研项目(CXY2010NH15);南京大学计算机软件新技术国家重点实验室开放基金项目(KFKT2010B17);江苏高校优势 学科建设工程项目(2013年)

Background Suppression of Small Infrared Target Image Based on Nonsubsampled Complex Contourlet Transform and GaussianWavelet Support Vector Regression

WU Yi-quan1,2,3, SONG Yu1   

  1. (1.College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing 210016, Jiangsu, China;2.Science and Technology on Electrooptic Control Laboratory, Luoyang 471009, Henan, China;3.State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, Jiangsu, China)
  • Received:2013-10-16 Revised:2013-10-16 Online:2015-06-02

摘要: 针对存在背景干扰和噪声情况下的红外目标图像背景抑制问题,提出了一种基于复无下采样轮廓波变换(NSCCT)和Gaussian小波支持向量回归(SVR)的背景抑制方法。该方法对红外目标图像进行NSCCT,然后根据其系数的相关特性去噪,从而抑制了大部分背景杂波;采用Gaussian小波SVR对去噪后的红外目标图像进行处理得到预测图像,并用去噪后图像减去预测图像得到残差图像,即背景抑制结果。针对红外目标图像进行了大量实验,并与近年来提出的3种背景预测方法,即基于最小二乘支持向量回归(LS-SVR)、基于SVR及基于最小二乘的红外目标图像背景抑制方法进行了比较,结果表明所提出的方法去噪效果好,背景抑制性能更优。针对存在背景干扰和噪声情况下的红外目标图像背景抑制问题,提出了一种基于复无下采样轮廓波变换(NSCCT)和Gaussian小波支持向量回归(SVR)的背景抑制方法。该方法对红外目标图像进行NSCCT,然后根据其系数的相关特性去噪,从而抑制了大部分背景杂波;采用Gaussian小波SVR对去噪后的红外目标图像进行处理得到预测图像,并用去噪后图像减去预测图像得到残差图像,即背景抑制结果。针对红外目标图像进行了大量实验,并与近年来提出的3种背景预测方法,即基于最小二乘支持向量回归(LS-SVR)、基于SVR及基于最小二乘的红外目标图像背景抑制方法进行了比较,结果表明所提出的方法去噪效果好,背景抑制性能更优。

关键词: 信息处理技术, 红外搜索与跟踪, 弱小目标检测, 背景抑制, 复无下采样轮廓波变换, Gaussian小波支持向量回归

Abstract: For the background suppression problem of dim target infrared image that contains background interference and noise, a new background suppression method based on nonsubsampled complex contourlet transform (NSCCT) and Gaussian wavelet support vector regression (SVR) is presented. With this method, the nonsubsampled complex contourlet transform is performed for the infrared target image, and then the correlation properties of NSCCT coefficients are used to de-noise the image so that the majority of background clutter is suppressed. Gaussian wavelet support vector regression is used to process the denoised infrared image to obtain the predicted image. The predicted image subtracted from the denoised image gives the residual image and the background is suppressed. A large number of experiments are done on infrared images including small targets, and the comparison is made with the background suppression methods of infrared target image based on least squares support vector regression, support vector regression and least squares. The experimental results show that the suggested method can get better de-noising result, and the performance of background suppression is superior.

Key words: information processing technology, infrared search and track, infrared dim target detection, background suppression, nonsubsampled complex contourlet transform, Gaussian wavelet support vector regression

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