Christian J. Schuler
Christian J. Schuler
Alumnus, Max Planck Institute for Intelligent Systems
Verified email at tuebingen.mpg.de
TitleCited byYear
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
7172012
Entropy search for information-efficient global optimization
P Hennig, CJ Schuler
Journal of Machine Learning Research 13 (Jun), 1809-1837, 2012
2692012
Fast removal of non-uniform camera shake
M Hirsch, CJ Schuler, S Harmeling, B Schölkopf
2011 International Conference on Computer Vision, 463-470, 2011
2492011
Learning to deblur
CJ Schuler, M Hirsch, S Harmeling, B Schölkopf
IEEE transactions on pattern analysis and machine intelligence 38 (7), 1439-1451, 2015
2042015
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
1682013
Learning how to combine internal and external denoising methods
HC Burger, C Schuler, S Harmeling
German Conference on Pattern Recognition, 121-130, 2013
652013
Non-stationary correction of optical aberrations
CJ Schuler, M Hirsch, S Harmeling, B Schölkopf
2011 International Conference on Computer Vision, 659-666, 2011
632011
Mask-specific inpainting with deep neural networks
R Köhler, C Schuler, B Schölkopf, S Harmeling
German Conference on Pattern Recognition, 523-534, 2014
512014
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
492012
Blind correction of optical aberrations
CJ Schuler, M Hirsch, S Harmeling, B Schölkopf
European Conference on Computer Vision, 187-200, 2012
332012
Thermal emission from finite photonic crystals
CJ Schuler, C Wolff, K Busch, M Florescu
Applied Physics Letters 95 (24), 241103, 2009
202009
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
192012
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
132013
Retrospective motion correction of magnitude-input MR images
A Loktyushin, C Schuler, K Scheffler, B Schölkopf
Medical Learning Meets Medical Imaging, 3-12, 2015
32015
Pregnancy up-regulated, nonubiquitous CaM kinase
LA Chodosh, HP Gardner
US Patent 7,041,495, 2006
32006
Machine Learning Approaches to Image Deconvolution
C Schuler
Eberhard Karls Universität Tübingen, 2017
12017
Determining computing device characteristics from computer network activity
A Asuncion, JC Schuler, GS Corrado, K Chen, Y Sheng
US Patent 9,372,914, 2016
12016
Probabilistic Progress Bars
M Kiefel, C Schuler, P Hennig
German Conference on Pattern Recognition, 331-341, 2014
12014
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
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Articles 1–20