4 papers
A Generative Variational Model for Inverse Problems in Imaging
Andreas Habring, Martin Holler
This paper is concerned with the development, analysis and numerical realization of a novel variational model for the regularization of inverse problems in imaging. The proposed mo…
Higher-order total variation approaches and generalisations
Kristian Bredies, Martin Holler
Over the last decades, the total variation (TV) evolved to one of the most broadly-used regularisation functionals for inverse problems, in particular for imaging applications. Whe…
Non-smooth variational regularization for processing manifold-valued data
Martin Holler, Andreas Weinmann
Many methods for processing scalar and vector valued images, volumes and other data in the context of inverse problems are based on variational formulations. Such formulations requ…
A convex variational model for learning convolutional image atoms from incomplete data
Antonin Chambolle, Martin Holler Thomas Pock
A variational model for learning convolutional image atoms from corrupted and/or incomplete data is introduced and analyzed both in function space and numerically. Building on lift…