Comparaison des performances d’algorithmes itératifs bayésiens basés sur trois classes de modèles a priori parcimonieux appliqués à la reconstruction tomographique
Résumé
The piecewise constant or homogeneous image reconstruction in the context of X-ray Computed Tomography is considered within a Bayesian approach. More precisely, the sparse transformation of such images is modelled with heavy tailed distributions expressed as Normal variance mixtures marginals. The derived iterative algorithms (via Joint Maximum A Posteriori) have identical updating expressions, except for the estimated variances. We show that the behaviour of the each algorithm is different in terms of sensibility to the model selection and reconstruction performances when applied in Computed Tomography.
Origine | Fichiers produits par l'(les) auteur(s) |
---|
Loading...