Carola-Bibiane Schönlieb

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Angelica I. Aviles-RiveroUniversity of CambridgeVerified email at cam.ac.uk
Martin BenningQueen Mary University of LondonVerified email at qmul.ac.uk
Martin BurgerFriedrich-Alexander Universität Erlangen-NürnbergVerified email at fau.de
Matthias J. EhrhardtMathematical Sciences, University of BathVerified email at bath.ac.uk
Juan Carlos De los ReyesCentro de Modelización Matemática (MODEMAT), Escuela Politécnica Nacional, EcuadorVerified email at epn.edu.ec
David CoomesProfessor of Forest Ecology and Conservation, University of CambridgeVerified email at cam.ac.uk
Xiaohao CaiSchool of Electronics and Computer Science, University of SouthamptonVerified email at soton.ac.uk
Luca CalatroniCNRS, I3S, Sophia-Antipolis, FranceVerified email at i3s.unice.fr
Jingwei LiangShanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
Jan LellmannUniversity of LuebeckVerified email at uni-luebeck.de
Sebastian LunzPhD Student, University of CambridgeVerified email at cam.ac.uk
Kostas PapafitsorosWeierstrass Institute for Applied Analysis and StochasticsVerified email at wias-berlin.de
Juheon LeeResearch ScientistVerified email at hp.com
Simone ParisottoImage Processing Researcher, siHealth Photonics SRLVerified email at sihealthphotonics.it
Bertram DüringProfessor of Mathematics, University of WarwickVerified email at warwick.ac.uk
Massimo FornasierProfessor of Applied Numerical Analysis, Technical University of MunichVerified email at ma.tum.de
Michael MoellerProfessor for Computer Vision, University of Siegen, GermanyVerified email at uni-siegen.de
Robert ToveyInria, ParisVerified email at inria.fr
Daniel CremersTechnical University of MunichVerified email at tum.de
Paul MidgleyProfessor, University of CambridgeVerified email at cam.ac.uk

Carola-Bibiane Schönlieb

DAMTP, University of Cambridge

Verified email at cam.ac.uk

Mathematics Computer Science Medical imaging inverse problems


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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, ... Nature Machine Intelligence 3 (3), 199-217, 2021	308	2021
Solving inverse problems using data-driven models S Arridge, P Maass, O Öktem, CB Schönlieb Acta Numerica 28, 1-174, 2019	292	2019
A combined first and second order variational approach for image reconstruction K Papafitsoros, CB Schönlieb Journal of mathematical imaging and vision 48 (2), 308-338, 2014	261	2014
Learning to diversify deep belief networks for hyperspectral image classification P Zhong, Z Gong, S Li, CB Schönlieb IEEE Transactions on Geoscience and Remote Sensing 55 (6), 3516-3530, 2017	241	2017
Cahn–Hilliard inpainting and a generalization for grayvalue images M Burger, L He, CB Schönlieb SIAM Journal on Imaging Sciences 2 (4), 1129-1167, 2009	177	2009
Adversarial regularizers in inverse problems S Lunz, O Öktem, CB Schönlieb Advances in neural information processing systems 31, 2018	144	2018
Unconditionally stable schemes for higher order inpainting A Bertozzi, CB Schönlieb Communications in Mathematical Sciences 9 (2), 413-457, 2011	131	2011
Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications A Chambolle, MJ Ehrhardt, P Richtárik, CB Schonlieb SIAM Journal on Optimization 28 (4), 2783-2808, 2018	130	2018
Partial differential equation methods for image inpainting CB Schönlieb Cambridge University Press, 2015	106	2015
Variational depth from focus reconstruction M Moeller, M Benning, C Schönlieb, D Cremers IEEE Transactions on Image Processing 24 (12), 5369-5378, 2015	98	2015
Imaging with Kantorovich--Rubinstein Discrepancy J Lellmann, DA Lorenz, C Schonlieb, T Valkonen SIAM Journal on Imaging Sciences 7 (4), 2833-2859, 2014	92	2014
Image denoising: learning the noise model via nonsmooth PDE-constrained optimization JC De los Reyes, CB Schönlieb Inverse Problems & Imaging 7 (4), 1183, 2013	92	2013
On the connection between adversarial robustness and saliency map interpretability C Etmann, S Lunz, P Maass, CB Schönlieb arXiv preprint arXiv:1905.04172, 2019	91	2019
Bilevel parameter learning for higher-order total variation regularisation models JC De los Reyes, CB Schönlieb, T Valkonen Journal of Mathematical Imaging and Vision 57 (1), 1-25, 2017	89	2017
Combined first and second order total variation inpainting using split Bregman K Papafitsoros, CB Schoenlieb, B Sengul Image Processing On Line, 2013	78	2013
Bilevel approaches for learning of variational imaging models L Calatroni, C Cao, JC De Los Reyes, CB Schönlieb, T Valkonen Variational Methods: In Imaging and Geometric Control 18 (252), 2, 2017	76	2017
A convergent overlapping domain decomposition method for total variation minimization M Fornasier, A Langer, CB Schönlieb Numerische Mathematik 116 (4), 645-685, 2010	67	2010
Subspace Correction Methods for Total Variation and -Minimization M Fornasier, CB Schönlieb SIAM Journal on Numerical Analysis 47 (5), 3397-3428, 2009	66	2009
Oriented diffusion filtering for enhancing low-quality fingerprint images C Gottschlich, CB Schönlieb IET biometrics 1 (2), 105-113, 2012	62	2012
Total variation regularization in measurement and image space for PET reconstruction M Burger, J Müller, E Papoutsellis, CB Schönlieb Inverse Problems 30 (10), 105003, 2014	61	2014

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