Sparse X-ray tomography using Bayesian inversion

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Date(s) - 19/02/2014
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) as the inverse problem of finding a Bayesian MAP estimate with Besov $B_{11}^1$ space prior or b) 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. »« >äki

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