Research menu
Jump to menu

Publications:  Dr Martin Benning

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 (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.
Return to top