Matthias J. Ehrhardt

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The Two-Dimensional Tree–Grid Method

Michael Günther, Igor Kossaczký, Matthias J. Ehrhardt, 2019 .

Corrigendum to "Incorporating structural prior information and sparsity into EIT using parallel level sets"

Ville Kolehmainen, Simon R. Arridge, Matthias J. Ehrhardt, 2020 .

Enhancing joint reconstruction and segmentation with non-convex Bregman iteration

Martin Benning, Lynn F. Gladden, Matthias J. Ehrhardt, 2018, Inverse Problems.

Synergistic multi-spectral CT reconstruction with directional total variation pdf

Matthias J. Ehrhardt, Evelyn Cueva, Alexander Meaney, 2021, Philosophical Transactions of the Royal Society A.

Joint Reconstruction for Multi-Modality Imaging with Common Structure

Matthias J. Ehrhardt, Matthias Joachim Ehrhardt, 2015 .

Robust Image Reconstruction With Misaligned Structural Information

Matthias J. Ehrhardt, Leon Bungert, 2020, IEEE Access.

Joint reconstruction of PET-MRI by parallel level sets

David Atkinson, Brian F. Hutton, Simon R. Arridge, 2014, 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

Erratum: Incorporating structural prior information and sparsity into eit using parallel level sets [1] (Inverse Problems and Imaging, (2019), 10.3934/ipi.2019015)

Simon R. Arridge, Ville Kolehmainen, Matthias J. Ehrhardt, 2020 .

Efficient Hyperparameter Tuning with Dynamic Accuracy Derivative-Free Optimization pdf

Matthias J. Ehrhardt, Lindon Roberts, Matthias Joachim Ehrhardt, 2020, ArXiv.

Robust Blind Image Fusion for Misaligned Hyperspectral Imaging Data

Matthias J. Ehrhardt, Leon Bungert, Rafael Reisenhofer, 2018 .

Geometric Numerical Integration Structure-Preserving Algorithms for QCD Simulations

Matthias J. Ehrhardt, Dmitry Shcherbakov, M. Ehrhardt, 2012 .

Supporting Material for Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation

Matthias J. Ehrhardt, Carola-Bibiane Schoenlieb, Leon Bungert, 2017 .

Inexact Derivative-Free Optimization for Bilevel Learning pdf

Matthias J. Ehrhardt, Lindon Roberts, Matthias Joachim Ehrhardt, 2020, Journal of Mathematical Imaging and Vision.

Multi-modality imaging with structure-promoting regularisers pdf

Matthias J. Ehrhardt, Matthias Joachim Ehrhardt, 2020, Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging.

A temporal multiscale approach for tissue quantification using MR Fingerprinting acquisitions

Mohammad Golbabaee, Matthias J. Ehrhardt, Samuel Cortinhas, 2020 .

On the Convergence of the Stochastic Primal-Dual Hybrid Gradient for Convex Optimization pdf

Matthias J. Ehrhardt, Matthias Joachim Ehrhardt, Eric B. Gutierrez, 2020, ArXiv.

Corrigendum to "Incorporating structural prior information and sparsity into EIT using parallel level sets"

Simon R. Arridge, Ville Kolehmainen, Matthias J. Ehrhardt, 2019, Inverse Problems & Imaging.

Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning

Brian F. Hutton, Kris Thielemans, Simon Arridge, 2018, IEEE Transactions on Medical Imaging.

Faster PET reconstruction with a stochastic primal-dual hybrid gradient method

Pawel Markiewicz, Antonin Chambolle, Peter Richtárik, 2017, Optical Engineering + Applications.

Versatile regularisation toolkit for iterative image reconstruction with proximal splitting algorithms

Kris Thielemans, Philip J. Withers, Martin Turner, 2019, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine.

Research data supporting "Deep learning as optimal control problems"

Elena Celledoni, Martin Benning, Brynjulf Owren, 2019 .

Correct A Priori Information Modelling for Sparse MRI Reconstruction

David Atkinson, Simon R. Arridge, Kris Thielemans, 2014 .

Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI

Matthias J. Ehrhardt, Matthias Joachim Ehrhardt, Claire Delplancke, 2021, SSVM.

A Geometric Integration Approach to Nonsmooth, Nonconvex Optimisation

Carola-Bibiane Schonlieb, G. R. W. Quispel, Matthias J. Ehrhardt, 2018, Foundations of Computational Mathematics.

Equivariant neural networks for inverse problems

Elena Celledoni, Christian Etmann, Carola-Bibiane Schonlieb, 2021, Inverse problems.

Synergistic multi-spectral CT reconstruction with directional total variation

Samuli Siltanen, Matthias J. Ehrhardt, Evelyn Cueva, 2021, Philosophical Transactions of the Royal Society A.

Choose Your Path Wisely: Gradient Descent in a Bregman Distance Framework

Martin Benning, Carola-Bibiane Schonlieb, Matthias J. Ehrhardt, 2017, SIAM J. Imaging Sci..

Inexact Derivative-Free Optimization for Bilevel Learning

Matthias J. Ehrhardt, Lindon Roberts, 2021, J. Math. Imaging Vis..

Deep learning as optimal control problems

Carola-Bibiane Schönlieb, Elena Celledoni, Martin Benning, 2021, IFAC-PapersOnLine.

Regularising Inverse Problems with Generative Machine Learning Models pdf

Neill D. F. Campbell, Matthias J. Ehrhardt, Margaret Duff, 2021, ArXiv.

Performance evaluation of MAP algorithms with different penalties, object geometries and noise levels

Brian F. Hutton, Kris Thielemans, Simon Arridge, 2015, 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

Gradient descent in a generalised Bregman distance framework

Martin Benning, Carola-Bibiane Schonlieb, Matthias J. Ehrhardt, 2016 .

Preface to special issue on joint reconstruction and multi-modality/multi-spectral imaging

Simon R. Arridge, Martin Burger, Matthias J. Ehrhardt, 2020, Inverse Problems.

Joint reconstruction of PET-MRI by exploiting structural similarity

David Atkinson, Brian F. Hutton, Simon R. Arridge, 2014, Inverse Problems.