X-ray Computed Tomography using a sparsity enforcing prior model based on Haar transformation in a Bayesian framework - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Fundamenta Informaticae Année : 2017

X-ray Computed Tomography using a sparsity enforcing prior model based on Haar transformation in a Bayesian framework

Li Wang
  • Fonction : Auteur
  • PersonId : 15096
  • IdHAL : li-wang-l2s
Nicolas Gac

Résumé

X-ray Computed Tomography (CT) has become a hot topic in both medical and industrial applications in recent decades. Reconstruction by using a limited number of projections is a significant research domain. In this paper, we propose to solve the X-ray CT reconstruction problem by using the Bayesian approach with a hierarchical structured prior model basing on the multilevel Haar transformation. In the proposed model, the multilevel Haar transformation is used as the sparse representation of a piecewise continuous image, and a generalized Student-t distribution is used to enforce its sparsity. The simulation results compare the performance of the proposed method with some state-of-the-art methods.
Fichier principal
Vignette du fichier
FI_LW_AMD_NG_revised.pdf (677.49 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01490523 , version 1 (15-03-2017)

Identifiants

Citer

Li Wang, Ali Mohammad-Djafari, Nicolas Gac. X-ray Computed Tomography using a sparsity enforcing prior model based on Haar transformation in a Bayesian framework. Fundamenta Informaticae, 2017, 155 (4), pp.449-480. ⟨10.3233/FI-2017-1594⟩. ⟨hal-01490523⟩
301 Consultations
220 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More