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Semi-unsupervised Bayesian convex image restoration with location mixture of Gaussian

Abstract : Convex image restoration is a major field in inverse problems. The problem is often addressed by hand-tuning hyper-parameters. We propose an incremental contribution about a Bayesian approach where a convex field is constructed via Location Mixture of Gaussian and the estimator computed with a fast MCMC algorithm. Main contributions are a new field with several operator avoiding crosslike artifacts and a fallback sampling algorithm to prevent numerical errors. Results, in comparison to standard supervised results, have equivalent quality in a quasi-unsupervised approach and go with uncertainty quantification.
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01705206
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Submitted on : Friday, February 9, 2018 - 11:00:03 AM
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François Orieux, Raphael Chinchilla. Semi-unsupervised Bayesian convex image restoration with location mixture of Gaussian. 25th European Signal Processing Conference (EUSIPCO 2017), Aug 2017, Kos, Greece. ⟨10.23919/EUSIPCO.2017.8081309⟩. ⟨hal-01705206⟩

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