A novel method for the reconstruction of images from a finite set of Radon data is presented. The method is based on kernel approximation techniques, so that we can deal with data collected at arbitrary scattered locations. In particular, applications to missing data scenarios and greedy-based sparse selection of data will be discussed.

This presentation is part of Minisymposium “MS2 - Interpolation and Approximation Methods in Imaging (4 parts)”
organized by: Alessandra De Rossi (University of Torino) , Costanza Conti (University of Firenze) , Francesco Dell'Accio (University of Calabria) .

Authors:

Gabriele Santin (University of Stuttgart)

Keywords:

computed tomography, image reconstruction, machine learning