Thomas Köhler



Organisation


Lehrstuhl für Informatik 5 (Mustererkennung)


Publications (Download BibTeX)

Go to first page Go to previous page 1 of 3 Go to next page Go to last page

Diaz-Pinto, A., Morales, S., Naranjo, V., Köhler, T., Mossi, J.M., & Navea, A. (2019). CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomedical Engineering Online, 18. https://dx.doi.org/10.1186/s12938-019-0649-y
Schirrmacher, F., Köhler, T., Husvogt, L., Fujimoto, J.G., Hornegger, J., & Maier, A. (2018). Abstract: QuaSI - Quantile Sparse Image A Prior for Spatio-Temporal Denoising of Retinal OCT Data. Paper presentation at Bildverarbeitung für die Medizin 2018, Erlangen.
Schirrmacher, F., Köhler, T., & Riess, C. (2018). Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems.
Köhler, T., Bätz, M., Naderi Boldaji, F., Kaup, A., Maier, A., & Riess, C. (2018). Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data.
Sindel, A., Breininger, K., Käßer, J., Heß, A., Maier, A., & Köhler, T. (2018). Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests. In Proceedings of the 25th IEEE International Conference on Image Processing, ICIP 2018 (pp. 1453-1457). IEEE Computer Society.
Schirrmacher, F., Köhler, T., Endres, J., Lindenberger, T., Husvogt, L., Fujimoto, J.G.,... Maier, A. (2018). Temporal and Volumetric Denoising via Quantile Sparse Image Prior. Medical Image Analysis, 48(0), 131-146. https://dx.doi.org/10.1016/j.media.2018.06.002
Köhler, T., Bätz, M., Naderi Boldaji, F., Kaup, A., Maier, A., & Riess, C. (2017). Benchmarking Super-Resolution Algorithms on Real Data.
Schirrmacher, F., Köhler, T., Husvogt, L., Fujimoto, J.G., Hornegger, J., & Maier, A. (2017). QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017, Proceedings, Part II (pp. 83-91). Quebec City, QC, Canada: Springer Verlag.
Fischer, P., Pohl, T., Köhler, T., Maier, A., & Hornegger, J. (2015). A Robust Probabilistic Model for Motion Layer Separation in X-Ray Fluoroscopy. In 24th International Conference on Information Processing in Medical Imaging, IPMI 2015 (pp. 288-299). Isle of Skye, Scotland: Berlin: Springer Verlag.
Köhler, T., Jordan, J.M., Maier, A., & Hornegger, J. (2015). A Unified Bayesian Approach to Multi-Frame Super-Resolution and Single-Image Upsampling in Multi-Sensor Imaging. In Proceedings of the British Machine Vision Conference (BMVC) (pp. 143.1-143.12). Swansea, Wales: Swansea, Wales: BMVA Press.

Last updated on 2019-16-03 at 22:52