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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Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B.
Learning to Deblur
IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7):1439-1451, IEEE, 2016 (article)
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Harmeling, S., Hirsch, M., Sra, S., Schölkopf, B., Schuler, C.
Method and device for recovering a digital image from a sequence of observed digital images
European Patent, No. 11767924.1, November 2015 (patent)
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Schuler, C.
Machine Learning Approaches to Image Deconvolution
University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)
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Loktyushin, A., Schuler, C., Scheffler, K., Schölkopf, B.
Retrospective motion correction of magnitude-input MR images
First International Workshop on Machine Learning Meets Medical Imaging (MLMMI 2015), held in conjunction with ICML 2015, 9487, pages: 3-12, Lecture Notes in Computer Science, (Editors: K. K. Bhatia and H. Lombaert), Springer, July 2015 (conference)
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Kiefel, M., Schuler, C., Hennig, P.
Probabilistic Progress Bars
In Conference on Pattern Recognition (GCPR), 8753, pages: 331-341, Lecture Notes in Computer Science, (Editors: Jiang, X., Hornegger, J., and Koch, R.), Springer, GCPR, September 2014 (inproceedings)
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Schuler, C., Hirsch, M., Harmeling, S., Schölkopf, B.
Method and device for blind correction of optical aberrations in a digital image
International Patent Application, No. PCT/EP2012/068868, April 2014 (patent)
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Köhler, R., Schuler, C., Schölkopf, B., Harmeling, S.
Mask-Specific Inpainting with Deep Neural Networks
In Pattern Recognition (GCPR 2014), pages: 523-534, (Editors: X Jiang, J Hornegger, and R Koch), Springer, 2014, Lecture Notes in Computer Science (inproceedings)
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Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B.
Learning to Deblur
In NIPS 2014 Deep Learning and Representation Learning Workshop, 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)
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Schuler, C., Burger, H., Harmeling, S., Schölkopf, B.
A machine learning approach for non-blind image deconvolution
In IEEE Conference on Computer Vision and Pattern Recognition, pages: 1067-1074, IEEE, CVPR, 2013 (inproceedings)
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