A Novel Convex Relaxation for Non-Binary Discrete Tomography

Add to your list(s) Download to your calendar using vCal

• Jan Kuske (University of Heidelberg, Germany)
• Friday 20 July 2018, 12:00-13:00
• Nuffield G19.

If you have a question about this talk, please contact Sergey Sergeev.

We present a novel convex relaxation and a corresponding inference algorithm for the non-binary discrete tomography problem, that is, reconstructing discrete-valued images from few linear measurements. In contrast to state of the art approaches that split the problem into a continuous reconstruction problem for the linear measurement constraints and a discrete labeling problem to enforce discrete-valued reconstructions, we propose a joint formulation that addresses both problems simultaneously, resulting in a tighter convex relaxation. For this purpose a constrained graphical model is set up and evaluated using a novel relaxation optimized by dual decomposition. We evaluate our approach experimentally and show superior solutions both mathematically (tighter relaxation) and experimentally in comparison to previously proposed relaxations.

This talk is part of the Optimisation and Numerical Analysis Seminars series.

This talk is included in these lists:

• Nuffield G19
• School of Mathematics Events

Note that ex-directory lists are not shown.