| Photo-realistic single image super-resolution using a generative adversarial network C Ledig, L Theis, F Huszár, J Caballero, A Cunningham, A Acosta, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 9617 | 2016 |
| Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network W Shi, J Caballero, F Huszár, J Totz, AP Aitken, R Bishop, D Rueckert, ... Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 4761 | 2016 |
| Lossy image compression with compressive autoencoders L Theis, W Shi, A Cunningham, F Huszár arXiv preprint arXiv:1703.00395, 2017 | 907 | 2017 |
| Real-time video super-resolution with spatio-temporal networks and motion compensation J Caballero, C Ledig, A Aitken, A Acosta, J Totz, Z Wang, W Shi Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 596 | 2017 |
| Amortised map inference for image super-resolution CK Sønderby, J Caballero, L Theis, W Shi, F Huszár arXiv preprint arXiv:1610.04490, 2016 | 446 | 2016 |
| Fast face-swap using convolutional neural networks I Korshunova, W Shi, J Dambre, L Theis Proceedings of the IEEE international conference on computer vision, 3677-3685, 2017 | 319 | 2017 |
| Cardiac image super-resolution with global correspondence using multi-atlas patchmatch W Shi, J Caballero, C Ledig, X Zhuang, W Bai, K Bhatia, ... Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th …, 2013 | 256 | 2013 |
| A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images W Bai, W Shi, DP O'regan, T Tong, H Wang, S Jamil-Copley, NS Peters, ... IEEE transactions on medical imaging 32 (7), 1302-1315, 2013 | 246 | 2013 |
| Right ventricle segmentation from cardiac MRI: a collation study C Petitjean, MA Zuluaga, W Bai, JN Dacher, D Grosgeorge, J Caudron, ... Medical image analysis 19 (1), 187-202, 2015 | 220 | 2015 |
| Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study TJW Dawes, A de Marvao, W Shi, T Fletcher, GMJ Watson, J Wharton, ... Radiology 283 (2), 381-390, 2017 | 186 | 2017 |
| Benchmarking framework for myocardial tracking and deformation algorithms: An open access database C Tobon-Gomez, M De Craene, K Mcleod, L Tautz, W Shi, A Hennemuth, ... Medical image analysis 17 (6), 632-648, 2013 | 177 | 2013 |
| Multi-atlas segmentation with augmented features for cardiac MR images W Bai, W Shi, C Ledig, D Rueckert Medical image analysis 19 (1), 98-109, 2015 | 170 | 2015 |
| Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand … R Karim, RJ Housden, M Balasubramaniam, Z Chen, D Perry, A Uddin, ... Journal of Cardiovascular Magnetic Resonance 15 (1), 1-17, 2013 | 158 | 2013 |
| Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize A Aitken, C Ledig, L Theis, J Caballero, Z Wang, W Shi arXiv preprint arXiv:1707.02937, 2017 | 151 | 2017 |
| Is the deconvolution layer the same as a convolutional layer? W Shi, J Caballero, L Theis, F Huszar, A Aitken, C Ledig, Z Wang arXiv preprint arXiv:1609.07009, 2016 | 148 | 2016 |
| A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion W Bai, W Shi, A de Marvao, TJW Dawes, DP O’Regan, SA Cook, ... Medical image analysis 26 (1), 133-145, 2015 | 126 | 2015 |
| The estimation of patient-specific cardiac diastolic functions from clinical measurements J Xi, P Lamata, S Niederer, S Land, W Shi, X Zhuang, S Ourselin, ... Medical image analysis 17 (2), 133-146, 2013 | 113 | 2013 |
| A comprehensive cardiac motion estimation framework using both untagged and 3D tagged MR images based on non-rigid registration. W Shi, X Zhuang, H Wang, D Luong, C Tobon-Gomez, P Edwards, ... Medical Imaging, IEEE Transactions on 31 (6), 1263-1275, 2012 | 99 | 2012 |
| Super resolution using a generative adversarial network W Shi, C Ledig, Z Wang, L Theis, F Huszar US Patent 11,024,009, 2021 | 98 | 2021 |
| Enhancing visual data using and augmenting model libraries Z Wang, RD Bishop, W Shi, J Caballero, AP Aitken, J Totz US Patent 10,499,069, 2019 | 82 | 2019 |
Daniel RueckertTechnical University of Munich and Imperial College LondonBestätigte E-Mail-Adresse bei tum.de
