Bayesian estimation of regularization and point spread function parameters for Wiener–Hunt deconvolution
JOSA A, Vol. 27, Issue 7, pp. 1593-1607 (2010)
http://dx.doi.org/10.1364/JOSAA.27.001593
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Abstract
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.
© 2010 Optical Society of America
OCIS Codes
(100.1830) Image processing : Deconvolution
(100.3020) Image processing : Image reconstruction-restoration
(100.3190) Image processing : Inverse problems
(150.1488) Machine vision : Calibration
ToC Category:
Image Processing
History
Original Manuscript: October 21, 2009
Revised Manuscript: March 5, 2010
Manuscript Accepted: April 15, 2010
Published: June 9, 2010
Virtual Issues
Vol. 5, Iss. 11 Virtual Journal for Biomedical Optics
Citation
François Orieux, Jean-François Giovannelli, and Thomas Rodet, "Bayesian estimation of regularization and point spread function parameters for Wiener–Hunt deconvolution," J. Opt. Soc. Am. A 27, 1593-1607 (2010)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=josaa-27-7-1593
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