Deconvolutionwith gaussian blur parameter and hyperparameters estimation

This paper proposes a Bayesian approach for unsupervised image deconvolution when the parameter of the gaussian PSF is unknown. The parameters of the regularization parameters are also unknown and jointly estimated with the other parameters. The solution is found by inferring on a global a posterior...

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Bibliographic Details
Main Authors: Orieux, F, Giovannelli, J.-F, Rodet, T
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Computational modeling
Deconvolution
full-bayesian approach
Image restoration
Instruments
Medical simulation
Monte-Carlo Markov chain
myopic deconvolution
Optical imaging
Parameter estimation
Pixel
Shape
unsupervised deconvolution
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Summary:This paper proposes a Bayesian approach for unsupervised image deconvolution when the parameter of the gaussian PSF is unknown. The parameters of the regularization parameters are also unknown and jointly estimated with the other parameters. The solution is found by inferring on a global a posteriori law for unknown object and parameters. The estimate is chosen in the sense of the posterior mean, numerically calculated by means of a Monte-Carlo Markov chain algorithm. The computation is efficiently done in Fourier space and the practicability of the method is shown on simulated examples. Results show high-frequencies restoration in the estimated image with correct estimation of the hyperparameters and instrument parameters.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2010.5495444