Christian Schuler
Position: PhD Student
Room no.: 203
Phone: +49 7071 601 531
Fax: +49 7071 601 552

I work on empirical inference on image data. Taking a photo is a measurement process, which can be affected by different types of errors. This includes motion blur, optical aberrations, and noise.

With prior knowledge of both the typical image content and the source of corruption, the measurement error can be removed to a large extent. In particular, I'm interested in using machine learning techniques to extract and model this prior knowledge.

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Conference Papers
  • M. Kiefel, C. H. Schuler, P. Hennig (2014). Probabilistic Progress Bars In: Pattern Recognition - 36th German Conference GCPR, LNCS Vol. 8753, (Ed) Jiang, X., Hornegger, J., and Koch, R., Springer, 331-341, GCPR 2014, (In Proceedings)
Conference Papers
  • HC. Burger, CJ. Schuler, S. Harmeling (2012). Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms State: submitted, (Article)
Conference Papers
  • HC. Burger, CJ. Schuler, S. Harmeling (2012). Image denoising: Can plain Neural Networks compete with BM3D? 2392 - 2399, 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), (In Proceedings)
  • CJ. Schuler, M. Hirsch, S. Harmeling, B. Schölkopf (2012). Blind Correction of Optical Aberrations In: Computer Vision - ECCV 2012, LNCS Vol. 7574, (Ed) A Fitzgibbon, S Lazebnik, P Perona, Y Sato, and C Schmid, Springer, Berlin, Germany, 187-200, ISBN: 978-3-642-33711-6, 12th IEEE European Conference on Computer Vision, ECCV 2012, (In Proceedings)
Conference Papers
Technical Reports