Inverse Scale Space Iterations for Non-convex Variational Problems Using Functional Lifting.

Danielle Bednarski, Jan Lellmann

Abstract

Non-linear filtering approaches allow to obtain decompositions of images with respect to a non-classical notion of scale. The associated inverse scale space flow can be obtained using the classical Bregman iteration applied to a convex, absolutely one-homogeneous regularizer. In order to extend these approaches to general energies with non-convex data term, we apply the Bregman iteration to a lifted version of the functional with sublabel-accurate discretization. We provide a condition for the subgradients of the regularizer under which this lifted iteration reduces to the standard Bregman iteration. We show experimental results for the convex and non-convex case.
OriginalspracheEnglisch
TitelScale Space and Variational Methods in Computer Vision
Erscheinungsdatum2021
Seiten229-241
DOIs
PublikationsstatusVeröffentlicht - 2021

Fingerprint

Untersuchen Sie die Forschungsthemen von „Inverse Scale Space Iterations for Non-convex Variational Problems Using Functional Lifting.“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitieren