Affiliations: Laboratoire des Signaux et Système, CentraleSupélec, CNRS, Université Paris Sud, Université Paris-Saclay, 3, Rue Joliot-Curie, 91192 Gif-sur-Yvette, France. [email protected], [email protected], [email protected]
Correspondence:
[†]
Address for correspondence: Laboratoire des Signaux et Système, CentraleSupélec, CNRS, Université Paris Sud, Université Paris-Saclay, 3, Rue Joliot-Curie, 91192 Gif-sur-Yvette, France
Note: [*] Thanks to China Scholarship Council for funding
Abstract: 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.
Keywords: Computed Tomography (CT), Bayesian Approach, Hierarchical Model, Generalized Student-t distribution, Joint Maximum A Posterior (JMAP)
