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

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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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hal-01705206 , version 1 (09-02-2018)

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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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