Performance comparison of Bayesian iterative algorithms for three classes of sparsity enforcing priors with application in computed tomography

Abstract : 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.
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Mircea Dumitru, Wang Li, Nicolas Gac, Ali Mohammad-Djafari. Performance comparison of Bayesian iterative algorithms for three classes of sparsity enforcing priors with application in computed tomography. 2017 IEEE International Conference on Image Processing, Sep 2017, Beijing, China. ⟨hal-01568337⟩

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