计算机应用研究2012，Vol.29Issue(7)：2722-2724,2736,4.DOI:10.3969/j.issn.1001-3695.2012.07.088
基于贝叶斯匹配追踪的SAR图像重构
SAR image reconstruction based on Bayesian matching pursuit
摘要
Abstract
Based on sparse learning and CS theory,this paper proposed a new SAR image reconstruction method. The process of image reconstruction was treated as a linear regression problem and the image to reconstruction was the unknown parameters of the regression model. It used Gaussian mixture parameters to predefine the prior conditional density of the unknown parame-ters in order to confine the sparsity. A set of model could be achieved that could be used to reconstruct the image in sense of MMSE. When the hyperparamelers were unknown,the method based on EM could be used to estimate. Simulation results indi-cate that the Bayesian matching pursuit based method can get a precisely reconstructed image and the details can be preserved.关键词
压缩感知/SAR图像/高斯混合参数/贝叶斯/EMKey words
compressive sensing( CS)/ SAR image/ Gaussian mixture parameter/ Bayesian/ EM分类
信息技术与安全科学引用本文复制引用
徐建平,皮亦鸣..基于贝叶斯匹配追踪的SAR图像重构[J].计算机应用研究,2012,29(7):2722-2724,2736,4.基金项目
中央高校基础研究基金资助项目(ZYGX2009Z005) （ZYGX2009Z005）
国家自然科学基金资助项目(60772143) （60772143）
