| Joint MR-PET reconstruction using a multi-channel image regularizer F Knoll, M Holler, T Koesters, R Otazo, K Bredies, DK Sodickson IEEE transactions on medical imaging 36 (1), 1-16, 2016 | 130 | 2016 |
| Regularization of linear inverse problems with total generalized variation K Bredies, M Holler Journal of inverse and ill-posed problems 22 (6), 871-913, 2014 | 88 | 2014 |
| A total variation–based JPEG decompression model K Bredies, M Holler SIAM Journal on Imaging Sciences 5 (1), 366-393, 2012 | 86 | 2012 |
| A TGV-based framework for variational image decompression, zooming, and reconstruction. Part I: Analytics K Bredies, M Holler SIAM Journal on Imaging Sciences 8 (4), 2814-2850, 2015 | 84 | 2015 |
| Infimal convolution of total generalized variation functionals for dynamic MRI M Schloegl, M Holler, A Schwarzl, K Bredies, R Stollberger Magnetic resonance in medicine 78 (1), 142-155, 2017 | 71 | 2017 |
| On infimal convolution of TV-type functionals and applications to video and image reconstruction M Holler, K Kunisch SIAM Journal on Imaging Sciences 7 (4), 2258-2300, 2014 | 66 | 2014 |
| Image-based personalization of cardiac anatomy for coupled electromechanical modeling A Crozier, CM Augustin, A Neic, AJ Prassl, M Holler, TE Fastl, ... Annals of biomedical engineering 44, 58-70, 2016 | 62 | 2016 |
| Evaluation of parallel level sets and Bowsher’s method as segmentation-free anatomical priors for time-of-flight PET reconstruction G Schramm, M Holler, A Rezaei, K Vunckx, F Knoll, K Bredies, F Boada, ... IEEE transactions on medical imaging 37 (2), 590-603, 2017 | 58 | 2017 |
| Total generalized variation regularization for multi-modal electron tomography R Huber, G Haberfehlner, M Holler, G Kothleitner, K Bredies Nanoscale 11 (12), 5617-5632, 2019 | 41 | 2019 |
| Higher-order total variation approaches and generalisations K Bredies, M Holler Inverse Problems 36 (12), 123001, 2020 | 40 | 2020 |
| Total generalized variation for manifold-valued data K Bredies, M Holler, M Storath, A Weinmann SIAM Journal on Imaging Sciences 11 (3), 1785-1848, 2018 | 37 | 2018 |
| A function space framework for structural total variation regularization with applications in inverse problems M Hintermüller, M Holler, K Papafitsoros Inverse Problems 34 (6), 064002, 2018 | 28 | 2018 |
| Ultrafast 3D Bloch–Siegert B‐mapping using variational modeling A Lesch, M Schlöegl, M Holler, K Bredies, R Stollberger Magnetic resonance in medicine 81 (2), 881-892, 2019 | 22 | 2019 |
| A pointwise characterization of the subdifferential of the total variation functional K Bredies, M Holler arXiv preprint arXiv:1609.08918, 2016 | 21 | 2016 |
| A TGV regularized wavelet based zooming model K Bredies, M Holler International Conference on Scale Space and Variational Methods in Computer …, 2013 | 19 | 2013 |
| Coupled regularization with multiple data discrepancies M Holler, R Huber, F Knoll Inverse problems 34 (8), 084003, 2018 | 17 | 2018 |
| Artifact-free decompression and zooming of JPEG compressed images with total generalized variation K Bredies, M Holler Computer Vision, Imaging and Computer Graphics. Theory and Application: 7th …, 2013 | 17 | 2013 |
| A generative variational model for inverse problems in imaging A Habring, M Holler SIAM Journal on Mathematics of Data Science 4 (1), 306-335, 2022 | 16 | 2022 |
| Artifact-free JPEG decompression with total generalized variation K Bredies, M Holler International Conference on Computer Vision Theory and Applications 2, 12-21, 2012 | 16 | 2012 |
| Chest compression fraction calculation: A new, automated, robust method to identify periods of chest compressions from defibrillator data–Tested in Zoll X Series S Orlob, WJ Kern, B Alpers, M Schörghuber, A Bohn, M Holler, JT Gräsner, ... Resuscitation 172, 162-169, 2022 | 11 | 2022 |
