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

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.
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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, Polskie Towarzystwo Matematyczne, 2017, 155 (4), pp.449-480. ⟨10.3233/FI-2017-1594⟩. ⟨hal-01490523⟩

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