基于高斯混合模型的衍射成像算法  

A Diffraction Imaging Algorithm Based on Gaussian Mixture Model

作　　者：练秋生[1] 侯亚伟 苏月明 石保顺 LIAN Qiu-sheng;HOU Ya-wei;SU Yue-ming;SHI Bao-shun(School of Information Science and Engineering,Yanshan University,Qinhuangdao,He bei 066004,China)

机构地区：[1]燕山大学信息科学与工程学院,河北秦皇岛066004

出　　处：《电子学报》2018年第10期2347-2350,共4页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61471313);河北省自然科学基金(No.F2014203076)

摘　　要：编码衍射成像系统中记录的测量值丢失了相位,而相位含有关于图像的大部分结构信息.如何利用无相位测量值重构原始图像是相位恢复(Phase Retrieval,PR)算法面临的一个重要问题.由期望最大(EM)算法训练高斯混合模型(GMM)的最优参数,任一图像块可以选用GMM中某一模型分量最佳表示.基于该认识,本文利用GMM的统计特性融合数据保真项构造PR优化问题,并用加速邻近梯度法求解该问题.实验结果表明,该算法在噪声强度较大、编码衍射图案较少的情况下仍能获得较高质量的图像重构.The phase information of the recorded measurements is los t in the coded diffraction imaging system.However,the phase contains most of structural information about the image.How to reconstruct the original image from measurements without phase information is a crucial problem faced by the phase retrieval algorithms.The optimal parameters of the Gaussian mixture model(GMM)are trained by the expectation maximization(EM)algorithm.An image patch can be represented optimally by one of the components in the GMM model.Based on this fact,a PR optimization problem which fuses the statistical properties of GMM and the data fidelity term is formulated.Moreover,the accelerated proximal gr adient method is utilized to solve this problem.The experimental results show t hat the proposed method can achieve high-quality image at the case of few coded diffraction patterns and high noise levels.

关 键 词：相位恢复 编码衍射图案 高斯混合模型 加速邻近梯度 

分 类 号：TN911.73[电子电信—通信与信息系统]