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

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.
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https://hal.archives-ouvertes.fr/hal-00674508
Contributor : François Orieux <>
Submitted on : Thursday, March 8, 2012 - 9:56:38 AM
Last modification on : Monday, December 14, 2020 - 9:41:29 AM
Long-term archiving on: : Thursday, June 14, 2012 - 5:03:03 PM

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François Orieux, Jean-François Giovannelli, Thomas Rodet. Bayesian estimation of regularization and point spread function parameters for Wiener-Hunt deconvolution. Journal of the Optical Society of America. A Optics, Image Science, and Vision, Optical Society of America, 2010, pp.1593. ⟨10.1364/JOSAA.27.001593⟩. ⟨hal-00674508⟩

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