| Image denoising: Can plain neural networks compete with BM3D? HC Burger, CJ Schuler, S Harmeling 2012 IEEE conference on computer vision and pattern recognition, 2392-2399, 2012 | 717 | 2012 |
| Entropy search for information-efficient global optimization P Hennig, CJ Schuler Journal of Machine Learning Research 13 (Jun), 1809-1837, 2012 | 269 | 2012 |
| Fast removal of non-uniform camera shake M Hirsch, CJ Schuler, S Harmeling, B Schölkopf 2011 International Conference on Computer Vision, 463-470, 2011 | 249 | 2011 |
| Learning to deblur CJ Schuler, M Hirsch, S Harmeling, B Schölkopf IEEE transactions on pattern analysis and machine intelligence 38 (7), 1439-1451, 2015 | 204 | 2015 |
| A machine learning approach for non-blind image deconvolution CJ Schuler, HC Burger, S Harmeling, B Schölkopf Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, 2013 | 168 | 2013 |
| Learning how to combine internal and external denoising methods HC Burger, C Schuler, S Harmeling German Conference on Pattern Recognition, 121-130, 2013 | 65 | 2013 |
| Non-stationary correction of optical aberrations CJ Schuler, M Hirsch, S Harmeling, B Schölkopf 2011 International Conference on Computer Vision, 659-666, 2011 | 63 | 2011 |
| Mask-specific inpainting with deep neural networks R Köhler, C Schuler, B Schölkopf, S Harmeling German Conference on Pattern Recognition, 523-534, 2014 | 51 | 2014 |
| Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds HC Burger, CJ Schuler, S Harmeling arXiv preprint arXiv:1211.1544, 2012 | 49 | 2012 |
| Blind correction of optical aberrations CJ Schuler, M Hirsch, S Harmeling, B Schölkopf European Conference on Computer Vision, 187-200, 2012 | 33 | 2012 |
| Thermal emission from finite photonic crystals CJ Schuler, C Wolff, K Busch, M Florescu Applied Physics Letters 95 (24), 241103, 2009 | 20 | 2009 |
| Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms HC Burger, CJ Schuler, S Harmeling arXiv preprint arXiv:1211.1552, 2012 | 19 | 2012 |
| Method and device for recovering a digital image from a sequence of observed digital images S Harmeling, M Hirsch, S Sra, B Schölkopf, CJ Schuler US Patent App. 13/825,365, 2013 | 13 | 2013 |
| Retrospective motion correction of magnitude-input MR images A Loktyushin, C Schuler, K Scheffler, B Schölkopf Medical Learning Meets Medical Imaging, 3-12, 2015 | 3 | 2015 |
| Pregnancy up-regulated, nonubiquitous CaM kinase LA Chodosh, HP Gardner US Patent 7,041,495, 2006 | 3 | 2006 |
| Machine Learning Approaches to Image Deconvolution C Schuler Eberhard Karls Universität Tübingen, 2017 | 1 | 2017 |
| Determining computing device characteristics from computer network activity A Asuncion, JC Schuler, GS Corrado, K Chen, Y Sheng US Patent 9,372,914, 2016 | 1 | 2016 |
| Probabilistic Progress Bars M Kiefel, C Schuler, P Hennig German Conference on Pattern Recognition, 331-341, 2014 | 1 | 2014 |
| Information-Greedy Global Optimisation P Hennig, CJ Schuler NIPS 2011 Workshop on Bayesian Optimization, Experimental Design and Bandits, 2011 | | 2011 |
| Efficient Space-Variant Blind Deconvolution S Harmeling, M Hirsch, S Sra, C Schuler, B Schölkopf NIPS 2010 Workshop on Numerical Mathematics Challenges in Machine Learning …, 2010 | | 2010 |
Stefan HarmelingProfessor at Heinrich-Heine-Universität DüsseldorfVerified email at hhu.de
