| Inverting gradients-how easy is it to break privacy in federated learning? J Geiping, H Bauermeister, H Dröge, M Moeller Advances in Neural Information Processing Systems 33, 16937-16947, 2020 | 573 | 2020 |
| An adaptive IHS pan-sharpening method S Rahmani, M Strait, D Merkurjev, M Moeller, T Wittman IEEE Geoscience and Remote Sensing Letters 7 (4), 746-750, 2010 | 451 | 2010 |
| Learning proximal operators: Using denoising networks for regularizing inverse imaging problems T Meinhardt, M Moller, C Hazirbas, D Cremers Proceedings of the IEEE International Conference on Computer Vision, 1781-1790, 2017 | 322 | 2017 |
| A convex model for nonnegative matrix factorization and dimensionality reduction on physical space E Esser, M Moller, S Osher, G Sapiro, J Xin IEEE Transactions on Image Processing 21 (7), 3239-3252, 2012 | 194 | 2012 |
| Proximal backpropagation T Frerix, T Möllenhoff, M Moeller, D Cremers arXiv preprint arXiv:1706.04638, 2017 | 138 | 2017 |
| Witches' brew: Industrial scale data poisoning via gradient matching J Geiping, L Fowl, WR Huang, W Czaja, G Taylor, M Moeller, T Goldstein arXiv preprint arXiv:2009.02276, 2020 | 116 | 2020 |
| Variational depth from focus reconstruction M Moeller, M Benning, C Schönlieb, D Cremers IEEE Transactions on Image Processing 24 (12), 5369-5378, 2015 | 114 | 2015 |
| A variational approach for sharpening high dimensional images M Möller, T Wittman, AL Bertozzi, M Burger SIAM Journal on Imaging Sciences 5 (1), 150-178, 2012 | 98 | 2012 |
| Spectral decompositions using one-homogeneous functionals M Burger, G Gilboa, M Moeller, L Eckardt, D Cremers SIAM Journal on Imaging Sciences 9 (3), 1374-1408, 2016 | 81 | 2016 |
| A variational approach to hyperspectral image fusion M Moeller, T Wittman, AL Bertozzi Algorithms and Technologies for Multispectral, Hyperspectral, and …, 2009 | 73 | 2009 |
| Point-wise map recovery and refinement from functional correspondence E Rodolà, M Moeller, D Cremers arXiv preprint arXiv:1506.05603, 2015 | 72 | 2015 |
| Collaborative total variation: a general framework for vectorial TV models J Duran, M Moeller, C Sbert, D Cremers SIAM Journal on Imaging Sciences 9 (1), 116-151, 2016 | 70 | 2016 |
| The primal-dual hybrid gradient method for semiconvex splittings T Möllenhoff, E Strekalovskiy, M Moeller, D Cremers SIAM Journal on Imaging Sciences 8 (2), 827-857, 2015 | 57 | 2015 |
| An adaptive inverse scale space method for compressed sensing M Burger, M Möller, M Benning, S Osher Mathematics of Computation 82 (281), 269-299, 2013 | 57 | 2013 |
| Sublabel-accurate relaxation of nonconvex energies T Mollenhoff, E Laude, M Moeller, J Lellmann, D Cremers Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 47 | 2016 |
| What Doesn't Kill You Makes You Robust (er): How to Adversarially Train against Data Poisoning J Geiping, L Fowl, G Somepalli, M Goldblum, M Moeller, T Goldstein arXiv preprint arXiv:2102.13624, 2021 | 40 | 2021 |
| Stochastic training is not necessary for generalization J Geiping, M Goldblum, PE Pope, M Moeller, T Goldstein arXiv preprint arXiv:2109.14119, 2021 | 39 | 2021 |
| Variational wavelet pan-sharpening M Moeller, T Wittman, AL Bertozzi CAM Report, 08-81, 2008 | 36 | 2008 |
| Ds*: Tighter lifting-free convex relaxations for quadratic matching problems F Bernard, C Theobalt, M Moeller Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 35 | 2018 |
| Nonlinear spectral analysis via one-homogeneous functionals: overview and future prospects G Gilboa, M Moeller, M Burger Journal of Mathematical Imaging and Vision 56, 300-319, 2016 | 33 | 2016 |
