Tomographic imaging from limited projections is an ill-posed problem and reconstruction algorithms rely on regularization, often sparsity-based, to improve the quality of imaging. We present a spline framework for consistent discretization in tomographic reconstruction and demonstrate its advantages for sparse approximation. Our experiments provide comparisons with commonly-used techniques such as total variation based tomographic reconstruction.

This presentation is part of Minisymposium “MS47 - Splines in Imaging (3 parts)”
organized by: Carolina Beccari (Dept. Mathematics, University of Bologna) , Virginie Uhlmann (EPFL, Lausanne) , Michael Unser (EPFL, Lausanne) .

Authors:

Alireza Entezari (Department of Computer & Information Science & Engineering, University of Florida, Gainesville)

Kai Zhang (University of Florida )

Elham Sakhaee (University of Florida)

Keywords:

computed tomography, image reconstruction, image representation, inverse problems