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Deconvolution with gaussian blur parameter and hyperparameters estimation

Abstract : 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.
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Contributor : Thomas Rodet Connect in order to contact the contributor
Submitted on : Tuesday, March 10, 2020 - 10:36:01 AM
Last modification on : Saturday, May 1, 2021 - 3:38:41 AM
Long-term archiving on: : Thursday, June 11, 2020 - 2:37:06 PM


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François Orieux, Jean-François Giovannelli, Thomas Rodet. Deconvolution with gaussian blur parameter and hyperparameters estimation. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Mar 2010, Dallas, TX, United States. pp.1350 - 1353, ⟨10.1109/ICASSP.2010.5495444⟩. ⟨hal-00546590⟩



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