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  • 2020
  • 2019
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Author

  • Thomas Pock
2017

Variational Photoacoustic Image Reconstruction with Spatially Resolved Projection Data

Hammernik, K., Pock, T. & Nuster, R., 2017, Proc. SPIE. Vol. 10064.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2019

Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions

Johnson, P. M., Muckley, M. J., Bruno, M., Kobler, E., Hammernik, K., Pock, T. & Knoll, F., 2019, Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings. Knoll, F., Maier, A., Rueckert, D. & Ye, J. (eds.). Cham: Springer, p. 71-79 (Lecture Notes in Computer Science; vol. 11905).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Inverse GANs for accelerated MRI reconstruction

Narnhofer, D., Hammernik, K., Knoll, F. & Pock, T., 2019, Wavelets and Sparsity XVIII.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2017

L2 or not L2: Impact of Loss Function Design for Deep Learning MRI Reconstruction

Hammernik, K., Knoll, F., Sodickson, D. K. & Pock, T., 2017, Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM). 0687

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2018

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 conferenceAbstractResearchpeer-review

2017
2019

Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization

Mukkamala, M. C., Ochs, P., Pock, T. & Sabach, S., 2019, In : arXiv.org e-Print archive.

Research output: Contribution to journalArticleResearchpeer-review

2016

Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation

Reinbacher, C., Graber, G. & Pock, T., 23 Sep 2016.

Research output: Contribution to conferencePaperResearchpeer-review

File

Large-Scale Semantic 3D Reconstruction: an Adaptive Multi-Resolution Model for Multi-Class Volumetric Labeling

Blaha, M., Vogel, C., Richard, A., Wegner, J., Schindler, K. & Pock, T., 2016, Computer Vision and Pattern Recognition. IEEE Computer Society, p. 3176-3184 9 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Open Access
File
2019

Total roto-translational variation

Antonin, C. & Pock, T., 2019, In : Numerische Mathematik. p. 611-666

Research output: Contribution to journalArticleResearchpeer-review

Adaptive FISTA for nonconvex optimization

Ochs, P. & Pock, T., 1 Jan 2019, In : SIAM Journal on Optimization. 29, 4, p. 2482-2503 22 p.

Research output: Contribution to journalArticleResearchpeer-review

2017

End-to-End Training of Hybrid CNN-CRF Models for Semantic Segmentation using Structured Learning

Colovic, A., Knöbelreiter, P., Shekhovtsov, A. & Pock, T., 6 Feb 2017.

Research output: Contribution to conferencePaperResearchpeer-review

2016

End-to-End Training of Hybrid CNN-CRF Models for Stereo

Knöbelreiter, P., Reinbacher, C., Shekhovtsov, A. & Pock, T., 30 Nov 2016, In : arXiv.org e-Print archive.

Research output: Contribution to journalArticleResearchpeer-review

File
2017

A Deep Learning Architecture for Limited-Angle Computed Tomography Reconstruction

Hammernik, K., Würfl, T., Pock, T. & Maier, A., 2017, Bildverarbeitung für die Medizin 2017: Informatik aktuell. Springer Verlag Heidelberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2016

On the ergodic convergence rates of a first-order primaldual algorithm

Pock, T. & Antonin, C., 2016, In : Mathematical programming. 159, 1, p. 253–287

Research output: Contribution to journalArticleResearchpeer-review

2017

Learning a Variational Network for Reconstruction of Accelerated MRI Data

Hammernik, K., Klatzer, T., Kobler, E., Recht, M. P., Sodickson, D. K., Pock, T. & Knoll, F., 2017, In : Magnetic Resonance in Medicine.

Research output: Contribution to journalArticleResearchpeer-review

On the Influence of Sampling Pattern Design on Deep Learning-Based MRI Reconstruction

Hammernik, K., Knoll, F., Sodickson, D. K. & Pock, T., 2017, Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM). 0644

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2019

Crouzeix-Raviart approximation of the total variation on simplicial meshes

Pock, T. & Chambolle, A., 2019.

Research output: Working paperResearchpeer-review

2016

U-shaped Networks for Shape from Light Field

Heber, S., Yu, W. & Pock, T., Sep 2016, British Machine Vision Conference, BMVC 2016.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2017

Semantic 3D Reconstruction with Finite Element Bases

Vogel, C., Richard, A., Pock, T. & Schindler, K., 3 Sep 2017, 28th British Machine Vision Conference: BMVC 2017. Vol. 28. 28 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

File

Accelerated Knee Imaging Using a Deep Learning Based Reconstruction

Knoll, F., Hammernik, K., Garwood, E., Hirschmann, A., Rybak, L., Bruno, M., Block, K. T., Babb, J., Pock, T., Sodickson, D. K. & Recht, M. P., 2017, Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM). 0645

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2018

Variational Networks for Joint Image Reconstruction and Classification of Tumor Immune Cell Interactions in Melanoma Tissue Sections

Effland, A., Hölzel, M., Klatzer, T., Kobler, E., Landsberg, J., Neuhäuser, L., Pock, T. & Rumpf, M., 2018, Bildverarbeitung für die Medizin 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2017

A Primal Dual Network for Low-Level Vision Problems

Vogel, C. & Pock, T., 1 Sep 2017, German Conference on Pattern Recognition, 2017. Springer Berlin - Heidelberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

File
2018

A Review of Depth and Normal Fusion Algorithms

Antensteiner, D., Stolc, S. & Pock, T., 2018, In : Sensors .

Research output: Contribution to journalArticleResearchpeer-review

2017

Acceleration of the PDHGM on Partially Strongly Convex Functions

Valkonen, T. & Pock, T., 1 Nov 2017, In : Journal of Mathematical Imaging and Vision. 59, 3, p. 394-414 21 p.

Research output: Contribution to journalArticleResearchpeer-review

2020

Variational Networks: An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration

Effland, A., Kobler, E., Kunisch, K. & Pock, T., 11 Mar 2020, In : Journal of Mathematical Imaging and Vision. 62, p. 396-416

Research output: Contribution to journalArticleResearchpeer-review

Open Access
2018

A First-Order Primal-Dual Algorithm with Linesearch

Malitsky, Y. & Pock, T., 2018, In : SIAM Journal on Optimization. 28, 1, p. 411-432 22 p.

Research output: Contribution to journalArticleResearchpeer-review

Variational Fusion of Light Field and Photometric Stereo for Precise 3D Sensing within a Multi-Line Scan Framework

Antensteiner, D., Stolc, S. & Pock, T., 2018, Proceedings of the International Conference on Pattern Recognition (ICPR).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2017

Variational Networks: Connecting Variational Methods and Deep Learning

Kobler, E., Klatzer, T., Hammernik, K. & Pock, T., 2017, Pattern Recognition: German Conference, GCPR 2017, Proceedings. Springer, p. 281-293 (Lecture Notes in Computer Science; vol. 10496).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2018

Self-Supervised Learning for Stereo Reconstruction on Aerial Images

Knöbelreiter, P., Vogel, C. & Pock, T., 22 Jul 2018.

Research output: Contribution to conferencePaperResearchpeer-review

2017

Scalable Full Flow with Learned Binary Descriptors

Munda, G., Shekhovtsov, A., Knöbelreiter, P. & Pock, T., 13 Sep 2017.

Research output: Contribution to conferencePaperResearchpeer-review

2018

Learning Energy Based Inpainting for Optical Flow

Vogel, C., Knöbelreiter, P. & Pock, T., 4 Dec 2018.

Research output: Contribution to conferencePaperResearchpeer-review

2016

An introduction to continuous optimization for imaging

Chambolle, A. & Pock, T., 1 May 2016, In : Acta Numerica. 25, p. 161-319 159 p.

Research output: Contribution to journalReview articleResearchpeer-review

2018

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. 81, 1, p. 116-128 13 p.

Research output: Contribution to journalArticleResearchpeer-review

Variational Adversarial Networks for Accelerated MR Image Reconstruction

Hammernik, K., Kobler, E., Pock, T., Recht, M. P., Sodickson, D. K. & Knoll, F., 2018, p. 1091.

Research output: Contribution to conferenceAbstractResearchpeer-review

2019

Combining Variational Optimization and Deep Learning for efficient ASL image quality enhancement

Schwarzbach, M., Spann, S. M., Hammernik, K., Aigner, C. S., Pock, T. & Stollberger, R., 2019, Magnetic Resonance Materials in Physics, Biology and Medicine.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2018

Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow

Hofinger, M., Pock, T. & Moosbrugger, T., 5 Feb 2018, p. 97 - 104. 8 p.

Research output: Contribution to conferencePaperResearchpeer-review

Open Access
2019

On the estimation of the Wasserstein distance in generative models

Pinetz, T., Soukup, D. & Pock, T., 2019, German Conference on Pattern Recognition. p. 156-170

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2016

Convolutional Networks for Shape from Light Field

Heber, S. & Pock, T., 2016, IEEE Conference on Computer Vision and Pattern Recognition (2016).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2017

Trainable Regularization for Multi-frame Superresolution

Klatzer, T., Soukup, D., Kobler, E., Hammernik, K. & Pock, T., 2017, Pattern Recognition: German Conference, GCPR 2017, Proceedings. Roth, V. & Vetter, T. (eds.). Springer, p. 90-100 (Lecture Notes in Computer Science; vol. 10496).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

File

Neural EPI-Volume Networks for Shape from Light Field

Heber, S., Yu, W. & Pock, T., 2017, IEEE International Conference on Computer Vision (ICCV).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2018

Optimizing Wavelet Bases for Sparser Representations

Grandits, T. A. & Pock, T., 2018, Energy Minimization Methods in Computer Vision and Pattern Recognition: EMMCVPR 2017. Pelillo, M. & Hancock, E. (eds.). Cham: Springer, p. 249-262 (Lecture Notes in Computer Science; vol. 10746).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2019

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 proceedingConference contributionResearchpeer-review

Image Morphing in Deep Feature Spaces: Theory and Applications

Effland, A., Kobler, E., Pock, T., Rajkovic, M. & Rumpf, M., 2019, In : arXiv.org e-Print archive.

Research output: Contribution to journalArticleResearchpeer-review

Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction

Knoll, F., Hammernik, K., Zhang, C., Moeller, S., Pock, T., Sodickson, D. K. & Akcakaya, M., 2019, In : arXiv.org e-Print archive.

Research output: Contribution to journalArticleResearch

2017

Real-time panoramic tracking for event cameras

Reinbacher, C., Munda, G. & Pock, T., 16 Jun 2017, 2017 IEEE International Conference on Computational Photography, ICCP 2017 - Proceedings. Institute of Electrical and Electronics Engineers, 7951488

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2018

Assessment of the generalization of learned image reconstruction and the potential for transfer learning

Knoll, F., Hammernik, K., Pock, T., Sodickson, D. K. & Recht, M. P., 2018, p. 3376.

Research output: Contribution to conferenceAbstractResearchpeer-review

2019

Fast Decomposable Submodular Function Minimization using Constrained Total Variation

Kumar, KS., Bach, F. & Pock, T., 2019, In : arXiv.org e-Print archive.

Research output: Contribution to journalArticleResearchpeer-review

3D Fluid Flow Estimation with Integrated Particle Reconstruction

Lasinger, K., Vogel, C., Pock, T. & Schindler, K., 1 Jan 2019, Pattern Recognition - 40th German Conference, GCPR 2018, Proceedings. Fritz, M., Bruhn, A. & Brox, T. (eds.). Springer-Verlag Italia, p. 315-332 18 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11269 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2018

Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation

Munda, G., Reinbacher, C. & Pock, T., 2018, In : International Journal of Computer Vision. 126, 12, p. 1381-1393 13 p.

Research output: Contribution to journalArticleResearchpeer-review