b'Martin Burger'

research

∙ 04/02/2023

Resolution-Invariant Image Classification based on Fourier Neural Operators

In this paper we investigate the use of Fourier Neural Operators (FNOs) ...

0 Samira Kabri, et al. ∙

research

∙ 12/14/2022

Convergent Data-driven Regularizations for CT Reconstruction

The reconstruction of images from their corresponding noisy Radon transf...

0 Samira Kabri, et al. ∙

research

∙ 09/15/2022

Spectral Total-Variation Processing of Shapes – Theory and Applications

In this work we present a comprehensive analysis of total variation (TV)...

0 Jonathan Brokman, et al. ∙

research

∙ 07/01/2022

Analysis of Kinetic Models for Label Switching and Stochastic Gradient Descent

In this paper we provide a novel approach to the analysis of kinetic mod...

0 Martin Burger, et al. ∙

research

∙ 12/08/2021

Variational Regularization in Inverse Problems and Machine Learning

This paper discusses basic results and recent developments on variationa...

0 Martin Burger, et al. ∙

research

∙ 10/24/2021

Learning convex regularizers satisfying the variational source condition for inverse problems

Variational regularization has remained one of the most successful appro...

0 Subhadip Mukherjee, et al. ∙

research

∙ 07/13/2021

A Diffuse Interface Model for Cell Blebbing Including Membrane-Cortex Coupling with Linker Dynamics

The aim of this paper is to develop suitable models for the phenomenon o...

0 Philipp Werner, et al. ∙

research

∙ 06/04/2021

Neural Architecture Search via Bregman Iterations

We propose a novel strategy for Neural Architecture Search (NAS) based o...

0 Leon Bungert, et al. ∙

research

∙ 05/18/2021

Gradient Flows, Nonlinear Power Methods, and Computation of Nonlinear Eigenfunctions

This chapter describes how gradient flows and nonlinear power methods in...

0 Leon Bungert, et al. ∙

research

∙ 05/10/2021

A Bregman Learning Framework for Sparse Neural Networks

We propose a learning framework based on stochastic Bregman iterations t...

0 Leon Bungert, et al. ∙

research

∙ 02/24/2021

Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis

The susceptibility of deep neural networks to untrustworthy predictions,...

0 Leo Schwinn, et al. ∙

research

∙ 02/18/2021

Convex regularization in statistical inverse learning problems

We consider a statistical inverse learning problem, where the task is to...

0 Tatiana A. Bubba, et al. ∙

research

∙ 11/05/2020

Sampled Nonlocal Gradients for Stronger Adversarial Attacks

The vulnerability of deep neural networks to small and even imperceptibl...

0 Leo Schwinn, et al. ∙

research

∙ 09/21/2020

Mean-field optimal control for biological pattern formation

We propose a mean-field optimal control problem for the parameter identi...

0 Martin Burger, et al. ∙

research

∙ 07/10/2020

Adaptive Superresolution in Deconvolution of Sparse Peaks

The aim of this paper is to investigate superresolution in deconvolution...

0 Alexandra Koulouri, et al. ∙

research

∙ 06/22/2020

Sequentially optimized projections in X-ray imaging

This work applies Bayesian experimental design to selecting optimal proj...

0 Martin Burger, et al. ∙

research

∙ 05/28/2020

Variational regularisation for inverse problems with imperfect forward operators and general noise models

We study variational regularisation methods for inverse problems with im...

0 Leon Bungert, et al. ∙

research

∙ 12/05/2019

Total Variation Regularisation with Spatially Variable Lipschitz Constraints

We introduce a first order Total Variation type regulariser that decompo...

0 Martin Burger, et al. ∙

research

∙ 06/24/2019

An entropic Landweber method for linear ill-posed problems

The aim of this paper is to investigate the use of an entropic projectio...

0 Martin Burger, et al. ∙

research

∙ 06/24/2019

An entropic projection method for linear ill-posed problems

The aim of this paper is to investigate the use of an entropic projectio...

0 Martin Burger, et al. ∙

research

∙ 03/21/2019

Reconstruction Methods in THz Single-pixel Imaging

The aim of this paper is to discuss some advanced aspects of image recon...

0 Martin Burger, et al. ∙

research

∙ 03/12/2019

A total variation based regularizer promoting piecewise-Lipschitz reconstructions

We introduce a new regularizer in the total variation family that promot...

0 Martin Burger, et al. ∙

research

∙ 03/07/2019

Variational Graph Methods for Efficient Point Cloud Sparsification

In recent years new application areas have emerged in which one aims to ...

0 Daniel Tenbrinck, et al. ∙

research

∙ 02/27/2019

Computing Nonlinear Eigenfunctions via Gradient Flow Extinction

In this work we investigate the computation of nonlinear eigenfunctions ...

0 Leon Bungert, et al. ∙

research

∙ 06/26/2018

Convergence rates and structure of solutions of inverse problems with imperfect forward models

The goal of this paper is to further develop an approach to inverse prob...

0 Martin Burger, et al. ∙

research

∙ 05/09/2017

Adaptive Regularization of Some Inverse Problems in Image Analysis

We present an adaptive regularization scheme for optimizing composite en...

0 Byung-Woo Hong, et al. ∙

research

∙ 03/23/2017

Nonlinear Spectral Image Fusion

In this paper we demonstrate that the framework of nonlinear spectral de...

0 Martin Benning, et al. ∙

research

∙ 03/15/2017

Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation

Although block compressive sensing (BCS) makes it tractable to sense lar...

0 Trinh Van Chien, et al. ∙

research

∙ 09/08/2016

Adaptive Regularization in Convex Composite Optimization for Variational Imaging Problems

We propose an adaptive regularization scheme in a variational framework ...

0 Byung-Woo Hong, et al. ∙

research

∙ 07/12/2016

A Variational Model for Joint Motion Estimation and Image Reconstruction

The aim of this paper is to derive and analyze a variational model for t...

0 Martin Burger, et al. ∙

research

∙ 12/01/2015

On Optical Flow Models for Variational Motion Estimation

The aim of this paper is to discuss and evaluate total variation based r...

0 Martin Burger, et al. ∙

research

∙ 10/05/2015

Nonlinear Spectral Analysis via One-homogeneous Functionals - Overview and Future Prospects

We present in this paper the motivation and theory of nonlinear spectral...

0 Guy Gilboa, et al. ∙

research

∙ 12/13/2014

First order algorithms in variational image processing

Variational methods in imaging are nowadays developing towards a quite u...

0 Martin Burger, et al. ∙

Martin Burger

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