Bayesian estimation of regularization and point spread function parameters for Wiener-Hunt deconvolution

被引：63

作者：

Orieux, Francois ^{[1
]}

Giovannelli, Jean-Francois ^{[2
]}

Rodet, Thomas ^{[1
]}

机构：

[1] Univ Paris Sud 11, SUPELEC, CNRS, Lab Signaux & Syst, F-91192 Gif Sur Yvette, France

[2] Univ Bordeaux 1, ENSCPB, CNRS, Lab Integrat Mat Syst,ENSEIRB, F-33405 Talence, France

来源：

JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION | 2010年 / 27卷 / 07期

关键词：

ADAPTIVE OPTICS IMAGES; BLIND DECONVOLUTION; MYOPIC DECONVOLUTION; VARIATIONAL APPROACH; RESTORATION; FIELD; DISTRIBUTIONS;

D O I：

10.1364/JOSAA.27.001593

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

This paper tackles the problem of image deconvolution with joint estimation of point spread function (PSF) parameters and hyperparameters. Within a Bayesian framework, the solution is inferred via a global a posteriori law for unknown parameters and object. The estimate is chosen as the posterior mean, numerically calculated by means of a Monte Carlo Markov chain algorithm. The estimates are efficiently computed in the Fourier domain, and the effectiveness of the method is shown on simulated examples. Results show precise estimates for PSF parameters and hyperparameters as well as precise image estimates including restoration of high frequencies and spatial details, within a global and coherent approach. (C) 2010 Optical Society of America

引用

页码：1593 / 1607

页数：15

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