Research Output per year
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Image reconstruction
Engineering & Materials Science
Early Stopping
Mathematics
Image quality
Engineering & Materials Science
Image denoising
Engineering & Materials Science
Gradient Flow
Mathematics
Image Restoration
Mathematics
Deconvolution
Engineering & Materials Science
optimal control
Physics & Astronomy
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Research Output 2015 2019
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration
Effland, A., Kobler, E., Kunisch, K. & Pock, T., 2019, (Submitted) In : Journal of Mathematical Imaging and Vision. 32 p.Research output: Contribution to journal › Article › Research › peer-review
Early Stopping
Image denoising
Gradient Flow
Image Restoration
optimal control
Joint Reconstruction and Classification of Tumor Cells and Cell Interactions in Melanoma Tissue Sections with Synthesized Training Data
Effland, A., Kobler, E., Brandenburg, A., Klatzer, T., Neuhäuser, L., Hölzel, M., Landsberg, J., Pock, T. & Rumpf, M., 2019, In : International Journal of Computer Assisted Radiology and Surgery.Research output: Contribution to journal › Article › Research › peer-review
Time Discrete Geodesics in Deep Feature Spaces for Image Morphing
Effland, A., Kobler, E., Pock, T. & Rumpf, M., 2019, Scale Space and Variational Methods in Computer Vision: SSVM 2019. Lellmann, J., Burger, M. & Modersitzki, J. (eds.). Cham: Springer, p. 171-182 (Lecture Notes in Computer Science; vol. 11603).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Splines
Computer vision
Interpolation
Textures
Semantics
Analysis of the influence of deviations between training and test data in learned image reconstruction
Knoll, F., Hammernik, K., Kobler, E., Pock, T., Sodickson, D. K. & Recht, M. P., 2018.Research output: Contribution to conference › Abstract › Research › peer-review
Assessment of the generalization of learned image reconstruction and the potential for transfer learning
Knoll, F., Hammernik, K., Kobler, E., Pock, T., Recht, M. P. & Sodickson, D. K., 2018, In : Magnetic resonance in medicine. 13 p.Research output: Contribution to journal › Article › Research › peer-review
Activities 2018 2018
Variational Deep Learning for low-dose Computed Tomography
Erich Kobler (Speaker)18 Apr 2018
Activity: Talk or presentation › Talk at conference or symposium › Science to science
Learning Convex Regularizers for Image Reconstruction
Erich Kobler (Speaker)21 May 2018 → 25 May 2018
Activity: Talk or presentation › Invited talk at conference or symposium › Science to science
Variational networks: connecting variational methods and deep learning
Erich Kobler (Speaker)19 Mar 2018 → 23 Mar 2018
Activity: Talk or presentation › Invited talk at conference or symposium › Science to science
Learning for Compressed Sensing CT Reconstruction
Erich Kobler (Speaker)7 Jun 2018
Activity: Talk or presentation › Invited talk at conference or symposium › Science to science
Deep regularization for medical image analysis
Erich Kobler (Speaker)5 Jun 2018
Activity: Talk or presentation › Invited talk at conference or symposium › Science to science
Prizes
GCPR 2017 Best Paper Award
Erich Kobler (Recipient), Kerstin Hammernik (Recipient), Thomas Pock (Recipient) & Teresa Klatzer (Recipient), 15 Sep 2017
Prize: Prizes / Medals / Awards
Summa cum Laude Merit Award
Kerstin Hammernik (Recipient), Erich Kobler (Recipient), Thomas Pock (Recipient), Michael P Recht (Recipient), Daniel K Sodickson (Recipient) & Florian Knoll (Recipient), 2018
Prize: Prizes / Medals / Awards