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基于最大后验概率和鲁棒估计的图像恢复推广变分模型 被引量:4

A Generalized Variational Image Restoration Model Based on MAP and Robust Estimation
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摘要 基于最大后验概率和MRF理论的图像恢复描述框架,提出一个面向图像恢复的推广变分模型.模型中将噪声建模为广义正态分布,利用最大似然法估计形状参数自动选择合适的范数作为数据保真项;将图像梯度场的分布建模为混合密度类,利用鲁棒估计理论构造一个耦合全变差积分和Dirichlet积分的图像先验模型作为正则化项.利用推广泛函的凸性,讨论了该推广模型的最优解存在性.最后提出结合梯度加权最速下降和半点格式的数值迭代算法.实验结果表明,推广模型能自动区分污染图像中的噪声分布特性,对于高斯噪声和脉冲噪声的污染图像都能取得很好的恢复效果.通过计算峰值信噪比和边缘保护指数,分析和评价了推广模型与目前其他变分方法的性能. Starting from the viewpoint of maximum a posteriori (MAP) and MRF theory, a generalized variational functional model for image restoration is established in this paper. In this model, a hybrid image regularization term and image fidelity term are included. For image fidelity term, the distribution of noise is treated as the generalized Gaussian density, and thus the shape parameter is estimated by a maximum likelihood method to automatically choose the suitable L^p norm as the image fidelity criteria. Assuming that the gradient of images is a member of ε-contaminated normal distributions, an image prior model in the form of total variational integral and Dirichlet integral is proposed using the robust estimation method. Due to the convexity of the proposed energy functional, the existence of the minimizing solution of such functional is discussed. Finally a weighted gradient descent flow is developed for image de-noising with an iterative algorithm based on semi-point scheme. Experimental results show that the model can automatically distinguish the statistical distribution of noise and has good performance in image restoration, including Gaussian noise and impulse noise pollution. Compared with other variation methods, the performance analysis and evaluation is made by calculating the peak of signal noise ratio (PSNR) and peak of edge preservation ability (PEPA).
出处 《计算机研究与发展》 EI CSCD 北大核心 2007年第7期1105-1113,共9页 Journal of Computer Research and Development
基金 国家自然科学基金项目(60672074) 江苏省自然科学基金项目(BK2006569) 中国博士后科学基金项目(20060390285) 江苏省博士后科学基金项目(200601005B)
关键词 图像恢复 最大后验概率 鲁棒估计 变分法 偏微分方程 image restoration maximun a posteriori (MAP) robust estimation variation method partial differential equation (PDE)
作者简介 肖亮,1976年生,博士,博士后在站研究人员,中国计算机学会会员,主要研究方向为变分偏微分方程在图像处理中的应用、多尺度几何分析、虚拟现实与系统仿真.xtxiaoliang@163.com 韦志辉,1963年生,博士,教授,博士生导师,主要研究方向为小波分析、模式识别与图像处理. 吴慧中,1942年生,教授,博士生导师,主要研究方向为虚拟现实、智能CAD、计算机图形图像理论.
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参考文献19

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