| Noise2void-learning denoising from single noisy images A Krull, TO Buchholz, F Jug Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 1772 | 2019 |
| Learning 6d object pose estimation using 3d object coordinates E Brachmann, A Krull, F Michel, S Gumhold, J Shotton, C Rother European conference on computer vision, 536-551, 2014 | 1064 | 2014 |
| Dsac-differentiable ransac for camera localization E Brachmann, A Krull, S Nowozin, J Shotton, F Michel, S Gumhold, ... Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 821 | 2017 |
| Uncertainty-driven 6d pose estimation of objects and scenes from a single rgb image E Brachmann, F Michel, A Krull, MY Yang, S Gumhold Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 663 | 2016 |
| Democratising deep learning for microscopy with ZeroCostDL4Mic L Von Chamier, RF Laine, J Jukkala, C Spahn, D Krentzel, E Nehme, ... Nature communications 12 (1), 2276, 2021 | 561 | 2021 |
| Learning analysis-by-synthesis for 6D pose estimation in RGB-D images A Krull, E Brachmann, F Michel, MY Yang, S Gumhold, C Rother Proceedings of the IEEE international conference on computer vision, 954-962, 2015 | 272 | 2015 |
| Probabilistic noise2void: Unsupervised content-aware denoising A Krull, T Vičar, M Prakash, M Lalit, F Jug Frontiers in Computer Science 2, 5, 2020 | 219 | 2020 |
| Artificial-intelligence-driven scanning probe microscopy A Krull, P Hirsch, C Rother, A Schiffrin, C Krull Communications Physics 3 (1), 54, 2020 | 202 | 2020 |
| Content-aware image restoration for electron microscopy TO Buchholz, A Krull, R Shahidi, G Pigino, G Jékely, F Jug Methods in cell biology 152, 277-289, 2019 | 187 | 2019 |
| Global hypothesis generation for 6D object pose estimation F Michel, A Kirillov, E Brachmann, A Krull, S Gumhold, B Savchynskyy, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 159 | 2017 |
| Dynein motion switches from diffusive to directed upon cortical anchoring V Ananthanarayanan, M Schattat, SK Vogel, A Krull, N Pavin, ... Cell 153 (7), 1526-1536, 2013 | 111 | 2013 |
| Pivoting of microtubules around the spindle pole accelerates kinetochore capture I Kalinina, A Nandi, P Delivani, MR Chacón, AH Klemm, ... Nature cell biology 15 (1), 82-87, 2013 | 105 | 2013 |
| Removing structured noise with self-supervised blind-spot networks C Broaddus, A Krull, M Weigert, U Schmidt, G Myers 2020 IEEE 17th international symposium on biomedical imaging (ISBI), 159-163, 2020 | 97 | 2020 |
| Random forests versus neural networks—what's best for camera localization? D Massiceti, A Krull, E Brachmann, C Rother, PHS Torr 2017 IEEE international conference on robotics and automation (ICRA), 5118-5125, 2017 | 93 | 2017 |
| Imaging in focus: an introduction to denoising bioimages in the era of deep learning RF Laine, G Jacquemet, A Krull The international journal of biochemistry & cell biology 140, 106077, 2021 | 89 | 2021 |
| DenoiSeg: Joint Denoising and Segmentation TO Buchholz, M Prakash, D Schmidt, A Krull, F Jug European Conference on Computer Vision, 324-337, 2020 | 81 | 2020 |
| 6-dof model based tracking via object coordinate regression A Krull, F Michel, E Brachmann, S Gumhold, S Ihrke, C Rother Asian conference on computer vision, 384-399, 2014 | 76 | 2014 |
| Fully unsupervised diversity denoising with convolutional variational autoencoders M Prakash, A Krull, F Jug arXiv preprint arXiv:2006.06072, 2020 | 71 | 2020 |
| ZeroCostDL4Mic: an open platform to use Deep-Learning in Microscopy LV Chamier, RF Laine, J Jukkala, C Spahn, D Krentzel, E Nehme, ... BioRxiv, 2020.03. 20.000133, 2020 | 63 | 2020 |
| Poseagent: Budget-constrained 6d object pose estimation via reinforcement learning A Krull, E Brachmann, S Nowozin, F Michel, J Shotton, C Rother Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 63 | 2017 |
