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Communication Dans Un Congrès Année : 2016

A Student-t based sparsity enforcing hierarchical prior for linear inverse problems and its efficient Bayesian computation for 2D and 3D Computed Tomography

Résumé

In many imaging systems and in particular in X ray Computed Tomography (CT) the reconstruction problem can be written as a linear inverse problem. In these problems, one property which can often be exploited is sparsity of the solution in an appropriate basis. In this work we consider the Student-t model in its hierarchical Normal-Inverse Gamma with an appropriate dictionary based coefficient. Then, thanks to the hierarchical generative model of the observation, we derive the expression of the joint posterior law of all the unknowns and an alternate optimisation algorithm for obtaining the joint MAP solution. We then detail the implementation issues of this algorithms for parallel computation and show the results on real size 2D and 3D phantoms.
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Dates et versions

hal-01403787 , version 1 (27-11-2016)

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  • HAL Id : hal-01403787 , version 1

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Ali Mohammad-Djafari, Li Wang, Nicolas Gac, Folkert Bleichrodt. A Student-t based sparsity enforcing hierarchical prior for linear inverse problems and its efficient Bayesian computation for 2D and 3D Computed Tomography. International traveling workshop on interactions between sparse models and technology, Aug 2016, AALBORG, Denmark. ⟨hal-01403787⟩
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