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math.OC2021
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…
math.OC2019
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…
math.OC2018
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…