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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 journalArticleResearchpeer-review

Early Stopping
Image denoising
Gradient Flow
Image Restoration
optimal control

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.

Research output: Contribution to conferenceAbstractResearchpeer-review

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

Extrapolation
Gradient methods
Learning systems
Image processing
Experiments

Crouzeix-Raviart approximation of the total variation on simplicial meshes

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

Research output: Working paperResearchpeer-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

Magnetic resonance
Image reconstruction
Imaging techniques
Learning systems
Neural networks

Determination of the moisture change behavior of cross-laminated timber using an optical flow based computer vision technique

Hofinger, M., Pock, T. & Moosbrugger, T., 2019, In : Wood Material Science and Engineering. 14, 5, p. 332-341

Research output: Contribution to journalArticleResearchpeer-review

Optical flows
Timber
Computer vision
Moisture
Swelling

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

Set theory

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

Splines
Semantics
Experiments

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

Magnetic resonance
Image reconstruction
Sampling
Probability distributions
Deep learning

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

Knee
Anatomy
Magnetic Resonance Spectroscopy
Joints
Magnetic Resonance Imaging

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 journalArticleResearchpeer-review

Learned Collaborative Stereo Refinement

Knöbelreiter, P. & Pock, T., 2019, German Conference on Pattern Recognition. p. 3-17

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

Color
Gradient methods
Statistics

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

Cost functions
Probability distributions

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

Splines
Computer vision
Interpolation
Textures
Semantics

Total roto-translational variation

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

Research output: Contribution to journalArticleResearchpeer-review

Curvature
Energy
Divergence-free Vector Fields
Elastica
Variational Model
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

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

A Review of Depth and Normal Fusion Algorithms

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

Research output: Contribution to journalArticleResearchpeer-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. 81, 1, p. 116-128 13 p.

Research output: Contribution to journalArticleResearchpeer-review

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

Learning Energy Based Inpainting for Optical Flow

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

Research output: Contribution to conferencePaperResearchpeer-review

Optical flows
Feature extraction
Neural networks
Data storage equipment

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

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

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
Optical flows
Optical Flow
Humidity
Composite Materials
Atmospheric humidity

Self-Supervised Learning for Stereo Reconstruction on Aerial Images

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

Research output: Contribution to conferencePaperResearchpeer-review

Supervised learning
Antennas
Imaging techniques

Variational 3D-PIV with sparse descriptors

Lasinger, K., Vogel, C., Schindler, K. & Pock, T., 10 May 2018, In : Measurement science & technology. 29, 6, 14 p., 064010.

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

Variational Deep Learning for Low-Dose Computed Tomography

Kobler, E., Muckley, M., Chen, B., Knoll, F., Hammernik, K., Pock, T., Sodickson, D. & Otazo, R., 10 Sep 2018, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers, Vol. 2018-April. p. 6687-6691 5 p. 8462312

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

Tomography
Dosimetry
X rays
Image quality
Signal to noise ratio

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

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

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

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

convexity
Convex function
Convexity
Image processing
Primal-dual Method

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

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

Automated segmentation and morphometry of muscle fibers from haematoxylin-eosin-stained histological sections

Gerstenberger, C., Karbiener, M., Jaufer, N., Pock, T., Urschler, M. & Gugatschka, M., 19 Oct 2017.

Research output: Contribution to conferenceAbstractResearchpeer-review

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

Semantics
Neural networks
Image segmentation
Linear programming
Labeling

Eye Movements during silent and oral reading in a regular orthography: Basic characteristics and correlations with childhood cognitive abilities and adolescent reading skills

Krieber, M., Bartl-Pokorny, K. D., Pokorny, F. B., Zhang, D., Landerl, K., Körner, C., Pernkopf, F., Pock, T., Einspieler, C. & Marschik, P. B., 1 Feb 2017, In : PLoS ONE. 12, 2, 0170986.

Research output: Contribution to journalArticleResearchpeer-review

Aptitude
Eye movements
Eye Movements
childhood
Reading

Improved Accelerated Model-based Parameter Quantification with Total-Generalized- Variation Regularization.

Maier, O., Schlögl, M., Lesch, A. J., Petrovic, A., Holler, M., Pock, T. & Stollberger, R., 2017, (Accepted/In press).

Research output: Contribution to conferenceAbstractResearchpeer-review

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

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

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

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

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

Cameras
cameras
degrees of freedom
formulations
shift

Scalable Full Flow with Learned Binary Descriptors

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

Research output: Contribution to conferencePaperResearchpeer-review

Costs
Optical flows
Image resolution
Neural networks
Data storage equipment

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

Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration

Chen, Y. & Pock, T., 1 Jun 2017, In : IEEE Transactions on Pattern Analysis and Machine Intelligence. 39, 6, p. 1256-1272 17 p., 7527621.

Research output: Contribution to journalArticleResearchpeer-review

Image Restoration
Reaction-diffusion
Image reconstruction
Nonlinear Model
Diffusion Model

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
Cameras
Supervised learning
Inspection
Imaging techniques

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

Image reconstruction
Gradient methods
Neural networks
Deep learning
Experiments

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

image reconstruction
projection
noise measurement
visibility
vessels