One strategy in functional lifting is to co nsider probability measures on the label space of interest\, which can be discrete or continuous. Th e considered functionals often make use of a total variation regularizer which\, when lifted\, allow s for a dual formulation introducing a Lipschitz c onstraint. 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 ta lk\, we present a mathematical framework for total variation regularization that is inspired from th e theory of Optimal Transport and that covers all of the previous cases\, including probability meas ures on discrete and continuous label spaces and o n manifolds. This framework nicely explains the ab ove-mentioned Lipschitz constraint and comes with a robust theoretical background.

LOCATION:Seminar Room 1\, Newton Institute CONTACT:INI IT END:VEVENT END:VCALENDAR