Unsupervised sparsity enforcing iterative algorithms for 3D image reconstruction in X-ray Computed Tomography

Mircea Dumitru, Nicolas Gac, Li Wang, Ali Mohammad-Djafari


Published in:Fully3D 2017 Proceedings


sparsity, normal variance mixtures, Bayesian inference, computed tomography
Unsupervised iterative reconstruction algorithms based on a Bayesian approach for piecewise constant images are presented in this paper. Such images can be expressed via a sparse representation and the reconstruction problem can be addressed using sparsity enforcing priors. We focus on sparsity enforcing priors expressed as Normal variance mixture, considering three mixing distributions: Inverse Gamma distribution, corresponding to Student-t prior, general inverse Gaussian distribution with the real parameter fixed, corresponding to Normal-inverse Gaussian prior and Gamma distribution corresponding to Variance-Gamma prior. We present and discuss the corresponding iter-ative algorithms considering the Joint Maximum A Posteriori estimation showing simulations results for 3D X-ray Computed Tomography.
M. Dumitru
Laboratoire des signaux et systemes (L2S) - CentraleSupelec
N. Gac
Laboratoire des signaux et systemes (L2S) - CentraleSupelec
L. Wang
Laboratoire des signaux et systemes (L2S) - CentraleSupelec
A. Mohammad-Djafari
Laboratoire des signaux et systemes (L2S) - CentraleSupelec
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