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

François Orieux (1) , Raphael Chinchilla (1)

1 L2S - Laboratoire des signaux et systèmes

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.

Domains: Image Processing [eess.IV]; Signal and Image Processing; Signal and Image processing; Applications [stat.AP]

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⟩