# Sparse X-ray tomography using Bayesian inversion

Date(s) : 19/02/2014   iCal
14 h 00 min - 15 h 00 min

$“A sparsity promoting reconstruction method is studied in the context of X-ray tomography with limited X-ray projection data. The reconstruction method is based on minimizing a sum of l^2-norm and a l^1-norm. Especially considered is the l^1-norm of wavelet coefficients. Depending on the viewpoint this method can be considered either a\right) as the inverse problem of finding a Bayesian MAP estimate with Besov B_\left\{11\right\}^1 space prior or b\right) as a deterministic regularization with Besov norm penalty.$
A tailored large-scale primal-dual interior-point method is used to solve the associated constrained minimization problem. The selection of the regularization parameter (or prior parameter, depending on the viewpoint) is performed by a novel technique called the S-curve method. Numerical results are presented both from simulated and from real, experimental data.”

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