University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Optimal Transport-Based Total Variation for Functional Lifting and Q-Ball Imaging

Optimal Transport-Based Total Variation for Functional Lifting and Q-Ball Imaging

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

If you have a question about this talk, please contact info@newton.ac.uk.

VMVW01 - Variational methods, new optimisation techniques and new fast numerical algorithms

Co-Author: Jan Lellmann (Institute of Mathematics and Image Computing, University of Lübeck)

One strategy in functional lifting is to consider probability measures on the label space of interest, which can be discrete or continuous. The considered functionals often make use of a total variation regularizer which, when lifted, allows for a dual formulation introducing a Lipschitz constraint. In our recent work, we proposed to use a similar formulation of total variation for the restoration of so-called Q-Ball images. In this talk, we present a mathematical framework for total variation regularization that is inspired from the theory of Optimal Transport and that covers all of the previous cases, including probability measures on discrete and continuous label spaces and on manifolds. This framework nicely explains the above-mentioned Lipschitz constraint and comes with a robust theoretical background.

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2017 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity