45 citations · 166 across the 20 of their papers we have counts for
20 papers
Subgradient Langevin Methods for Sampling from Non-smooth Potentials
Andreas Habring, Martin Holler, Thomas Pock
This paper is concerned with sampling from probability distributions on admitting a density of the form , where with $K…
Exact Parameter Identification in PET Pharmacokinetic Modeling Using the Irreversible Two Tissue Compartment Model
Martin Holler, Erion Morina, Georg Schramm
This work is concerned with the identifiability of metabolic parameters from multi-region measurement data in quantitative PET imaging. It shows that, for the frequently used two-t…
A sparse optimization approach to infinite infimal convolution regularization
Kristian Bredies, Marcello Carioni, Martin Holler +2
In this paper we introduce the class of infinite infimal convolution functionals and apply these functionals to the regularization of ill-posed inverse problems. The proposed regul…
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging
Dominik Narnhofer, Andreas Habring, Martin Holler +1
In this work, a method for obtaining pixel-wise error bounds in Bayesian regularization of inverse imaging problems is introduced. The proposed method employs estimates of the post…
Unsupervised energy disaggregation via convolutional sparse coding
Christian Aarset, Andreas Habring, Martin Holler +1
In this work, a method for unsupervised energy disaggregation in private households equipped with smart meters is proposed. This method aims to classify power consumption as active…
Exact reconstruction and reconstruction from noisy data with anisotropic total variation
Martin Holler, Benedikt Wirth
It is well-known that point sources with sufficient mutual distance can be reconstructed exactly from finitely many Fourier measurements by solving a convex optimization problem wi…
Latent-space disentanglement with untrained generator networks for the isolation of different motion types in video data
Abdullah Abdullah, Martin Holler, Karl Kunisch +1
Isolating different types of motion in video data is a highly relevant problem in video analysis. Applications can be found, for example, in dynamic medical or biological imaging,…
A Note on the Regularity of Images Generated by Convolutional Neural Networks
Andreas Habring, Martin Holler
The regularity of images generated by convolutional neural networks, such as the U-net, generative networks, or the deep image prior, is analyzed. In a resolution-independent, infi…
Learning-informed parameter identification in nonlinear time-dependent PDEs
Christian Aarset, Martin Holler, Tram Thi Ngoc Nguyen
We introduce and analyze a method of learning-informed parameter identification for partial differential equations (PDEs) in an all-at-once framework. The underlying PDE model is f…
Fast and memory-efficient reconstruction of sparse Poisson data in listmode with non-smooth priors with application to time-of-flight PET
Georg Schramm, Martin Holler
Complete time of flight (TOF) sinograms of state-of-the-art TOF PET scanners have a large memory footprint. Currently, they contain ~4e9 data bins which amount to ~17GB in 32bit fl…
Dimension reduction, exact recovery, and error estimates for sparse reconstruction in phase space
Martin Holler, Alexander Schlüter, Benedikt Wirth
An important theme in modern inverse problems is the reconstruction of time-dependent data from only finitely many measurements. To obtain satisfactory reconstruction results in th…
Regularization Graphs -- A unified framework for variational regularization of inverse problems
Kristian Bredies, Marcello Carioni, Martin Holler
We introduce and study a mathematical framework for a broad class of regularization functionals for ill-posed inverse problems: Regularization Graphs. Regularization graphs allow t…
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…
Coupled regularization with multiple data discrepancies
Martin Holler, Richard Huber, Florian Knoll
We consider a class of regularization methods for inverse problems where a coupled regularization is employed for the simultaneous reconstruction of data from multiple sources. App…
A function space framework for structural total variation regularization with applications in inverse problems
Michael Hintermüller, Martin Holler, Kostas Papafitsoros
In this work, we introduce a function space setting for a wide class of structural/weighted total variation (TV) regularization methods motivated by their applications in inverse p…
Total Generalized Variation for Manifold-valued Data
K. Bredies, M. Holler, M. Storath +1
In this paper we introduce the notion of second-order total generalized variation (TGV) regularization for manifold-valued data in a discrete setting. We provide an axiomatic appro…
A pointwise characterization of the subdifferential of the total variation functional
K. Bredies, M. Holler
We derive a new pointwise characterization of the subdifferential of the total variation (TV) functional. It involves a full trace operator which maps certain - vectorfield…