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  1. Queen Mary University of London
  2. Research
  3. Publications

Publications: Dr Martin Benning

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Wang X, Benning M ( 2023 ) . A lifted Bregman formulation for the inversion of deep neural networks . Frontiers in Applied Mathematics and Statistics vol. 9 ,
Benning M, Riis ES ( 2023 ) . Bregman Methods for Large-Scale Optimization with Applications in Imaging . Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision ,
Khan A, Alwazzan O, Benning M, Slabaugh G ( 2023 ) . Sequential Segmentation of the Left Atrium and Atrial Scars Using a Multi-scale Weight Sharing Network and Boundary-Based Processing . vol. 13586 LNCS ,
Bonnici RS, Benning M, Saitis C ( 2022 ) . Timbre Transfer with Variational Auto Encoding and Cycle-Consistent Adversarial Networks . Conference: 2022 International Joint Conference on Neural Networks (IJCNN) vol. 00 , 1 - 8 .
Benning M, Celledoni E, Ehrhardt MJ, Owren B, Schönlieb C-B ( 2021 ) . Deep learning as optimal control problems . IFAC-PapersOnLine vol. 54 , ( 9 ) 620 - 623 .
Benning M, Betcke MM, Ehrhardt MJ, Schönlieb C-B ( 2021 ) . Choose Your Path Wisely: Gradient Descent in a Bregman Distance Framework . SIAM Journal on Imaging Sciences vol. 14 , ( 2 ) 814 - 843 .
Benning M, Riis ES ( 2021 ) . Bregman Methods for Large-Scale Optimisation with Applications in Imaging . Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging1 - 42 .
Corona V, Benning M, Gladden LF, Reci A, Sederman AJ, Schönliebs CB ( 2021 ) . Joint phase reconstruction and magnitude segmentation from velocity-encoded MRI data . Time-dependent Problems in Imaging and Parameter Identification ,
Tovey R, Johnstone DN, Collins SM, Lionheart WRB, Midgley PA, Benning M, Schönlieb C-B ( 2020 ) . Scanning electron diffraction tomography of strain . Inverse Problems
Sherry F, Benning M, Reyes JCDL, Graves MJ, Maierhofer G, Williams G, Schonlieb C-B, Ehrhardt MJ ( 2020 ) . Learning the Sampling Pattern for MRI . IEEE Transactions on Medical Imaging1 - 1 .
Benning M, Riis ES, Schönlieb C-B ( 2020 ) . Bregman Itoh–Abe Methods for Sparse Optimisation . Journal of Mathematical Imaging and Vision
Benning M, Celledoni E, Ehrhardt M, Owren B, Schhönlieb C-B ( 2019 ) . Deep learning as optimal control problems: models and numerical methods . Journal of Computational Dynamics vol. 6 , ( 2 ) 171 - 198 .
Burger M, Resmerita E, Benning M ( 2019 ) . An entropic Landweber method for linear ill-posed problems . Inverse Problems
Collins SM, MacArthur KE, Longley L, Tovey R, Benning M, Schönlieb CB, Bennett TD, Midgley PA ( 2019 ) . Phase diagrams of liquid-phase mixing in multi-component metal-organic framework glasses constructed by quantitative elemental nano-tomography . APL Materials vol. 7 , ( 9 )
Corona V, BENNING M, Ehrhardt M, Gladden L, Mair R, Reci A, Sederman A, Reichelt S et al. ( 2019 ) . Enhancing joint reconstruction and segmentation with non-convex Bregman iteration . Inverse Problems
Tovey R, Benning M, Brune C, Lagerwerf MJ, Collins SM, Leary RK, Midgley PA, Schonlieb C-B ( 2019 ) . Directional sinogram inpainting for limited angle tomography . INVERSE PROBLEMS vol. 35 , ( 2 ) Article ARTN 024004 ,
Benning M, Burger M ( 2018 ) . Modern regularization methods for inverse problems . Acta Numerica vol. 27 , 1–111 - 1–111 .
Schmidt MF, Benning M, nlieb C-BS ( 2018 ) . Inverse scale space decomposition . Inverse Problems vol. 34 , Article 4 , 045008 - 045008 .
Collins SM, Leary RK, Midgley PA, Tovey R, Benning M, Schönlieb C-B, Rez P, Treacy MMJ ( 2017 ) . Entropic Comparison of Atomic-Resolution Electron Tomography of Crystals and Amorphous Materials . Phys. Rev. Lett. vol. 119 , ( 16 ) 166101 - 166101 .
Benning M, Gilboa G, Grah JS, Schönlieb C-B ( 2017 ) . Learning Filter Functions in Regularisers by Minimising Quotients . Conference: Scale Space and Variational Methods in Computer Vision (SSVM) 2017511 - 523 .
Benning M, Möller M, Nossek RZ, Burger M, Cremers D, Gilboa G, Schönlieb C-B ( 2017 ) . Nonlinear Spectral Image Fusion . Conference: Scale Space and Variational Methods in Computer Vision (SSVM) 201741 - 53 .
Benning M, Betcke MM, Ehrhardt MJ, Schönlieb C-B ( 2017 ) . Gradient descent in a generalised Bregman distance framework . Geometric Numerical Integration and its Applications . Editors: Quispel, GRW, Bader, P, McLaren, DI, Tagami, D et al. , vol. 74 , 40 - 45 .
Ramskill NP, Bush I, Sederman AJ, Mantle MD, Benning M, Anger BC, Appel M, Gladden LF ( 2016 ) . Fast imaging of laboratory core floods using 3D compressed sensing RARE MRI . Journal of Magnetic Resonance vol. 270 , 187 - 197 .
Benning M, Gilboa G, Schönlieb C-B ( 2016 ) . Learning parametrised regularisation functions via quotient minimisation . PAMM vol. 16 , Article 1 , 933 - 936 .
Benning M, Knoll F, Schönlieb C-B, Valkonen T ( 2016 ) . Preconditioned ADMM with Nonlinear Operator Constraint . 117 - 126 .
Harbou EV, Fabich HT, Benning M, Tayler AB, Sederman AJ, Gladden LF, Holland DJ ( 2015 ) . Quantitative mapping of chemical compositions with MRI using compressed sensing . Journal of Magnetic Resonance vol. 261 , 27 - 37 .
Möller M, Benning M, Schönlieb C-B, Cremers D ( 2015 ) . Variational Depth From Focus Reconstruction . IEEE Transactions on Image Processing vol. 24 , Article 12 , 5369 - 5378 .
Saghi Z, Benning M, Leary R, Macias-Montero M, Borras A, Midgley PA ( 2015 ) . Reduced-dose and high-speed acquisition strategies for multi-dimensional electron microscopy . Advanced Structural and Chemical Imaging vol. 1 , Article 1 , 7 - 7 .
Heck C, Benning M, Modersitzki J ( 2015 ) . Joint Registration and Parameter Estimation of T1 Relaxation Times Using Variable Flip Angles . 215 - 220 .
Fabich HT, Benning M, Sederman AJ, Holland DJ ( 2014 ) . Ultrashort echo time (UTE) imaging using gradient pre-equalization and compressed sensing . Journal of Magnetic Resonance vol. 245 , 116 - 124 .
Tayler AB, Benning M, Sederman AJ, Holland DJ, Gladden LF ( 2014 ) . Ultrafast magnetic-resonance-imaging velocimetry of liquid-liquid systems: Overcoming chemical-shift artifacts using compressed sensing . Phys. Rev. E vol. 89 , ( 6 ) 063009 - 063009 .
Benning M, Gladden L, Holland D, Schönlieb C-B, Valkonen T ( 2014 ) . Phase reconstruction from velocity-encoded MRI measurements – A survey of sparsity-promoting variational approaches . Journal of Magnetic Resonance vol. 238 , 26 - 43 .
Benning M, Burger M ( 2013 ) . Ground states and singular vectors of convex variational regularization methods . Methods and Applications of Analysis vol. 20 , Article 4 , 295 - 334 .
Burger M, Möller M, Benning M, Osher S ( 2013 ) . An adaptive inverse scale space method for compressed sensing . Mathematics of Computation vol. 82 , Article 281 , 269 - 299 .
Benning M, Brune C, Burger M, Müller J ( 2013 ) . Higher-Order TV Methods—Enhancement via Bregman Iteration . Journal of Scientific Computing vol. 54 , Article 2 , 269 - 310 .
Benning M, Calatroni L, Düring B, Schönlieb C-B ( 2013 ) . A Primal-Dual Approach for a Total Variation Wasserstein Flow . 413 - 421 .
Benning M, Kösters T, Lamare F ( 2012 ) . Combined correction and reconstruction methods . Correction Techniques in Emission Tomography , Editors: Dawood, M, Jiang, X, Schäfers, K , CRC Press
Engbers R, Benning M, Heins P, Schäfers K, Burger M ( 2011 ) . Sparse recovery in myocardial blood flow quantification via PET . 2011 IEEE Nuclear Science Symposium Conference Record . 3742 - 3744 .
Benning M, Burger M ( 2011 ) . Error estimates for general fidelities . Electronic Transactions on Numerical Analysis vol. 38 , Article 44-68 , 77 - 77 .
Benning M, Burger M ( 2011 ) . Error estimates for general fidelities . Electronic Transactions on Numerical Analysis vol. 38 , 44 - 68 .
Benning M ( 2011 ) . Singular Regularization of Inverse Problems: Bregman Distances and their Applications to Variational Frameworks with Singular Regularization Energies .
Benning M, Heins P, Burger M ( 2010 ) . A Solver for Dynamic PET Reconstructions based on Forward-Backward-Splitting . AIP Conference Proceedings . vol. 1281 , 1967 - 1970 .
Benning M, Kösters T, Wübbeling F, Schäfers K, Burger M ( 2008 ) . A nonlinear variational method for improved quantification of myocardial blood flow using dynamic H215O PET . 2008 IEEE Nuclear Science Symposium Conference Record . 4472 - 4477 .
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