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Matrix-free X-ray tomography

This page contains computational resources related to the book
Linear and Nonlinear Inverse Problems with Practical Applications
written by Jennifer Mueller and Samuli Siltanen and published by SIAM in 2012.
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Here we solve tomographic problems without constructing the measurement matrix A at all.

Simulate tomographic data without inverse crime

This routine uses the radon.m function of Matlab's Image processing toolbox to create a sinogram:

XRA_NoCrimeData_comp.m

Compute reconstruction using filtered back-projection

XRB_FBP_comp.m

Compute reconstruction using matrix-free iterative Tikhonov regularization

XRC_Tikhonov_comp.m, XRC_Tikhonov_plot.m

Compute reconstruction using matrix-free iterative (approximate) total variation regularization

Here are the main computation and plot routines:

XRD_aTV_comp.m, XRD_aTV_plot.m

You will also need these files:

XR_aTV_feval.m, XR_aTV_fgrad.m, XR_aTV_grad.m, XR_aTV.m, XR_misfit_grad.m, XR_misfit.m

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