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2015

A convex, lower semi-continuous approximation of euler’s elastica energy

Pock, T., Wirth, B. & Bredies, K., 2015, In : SIAM journal on mathematical analysis. 47, 1, p. 566-613

Research output: Contribution to journalArticleResearchpeer-review

Bilevel optimization with nonsmooth lower level problems

Ochs, P., Ranftl, R., Brox, T. & Pock, T., 2015, (Accepted/In press) Proceedings of SSVM 2015. .

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

Continuous Hyper-parameter Learning for Support Vector Machines

Klatzer, T. & Pock, T., 2015, (Accepted/In press) Computer Vision Winter Workshop.

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

Open Access
File

Depth Restoration via Joint Training of a Global Regression Model and CNNs

Riegler, G., Ranftl, R., Rüther, M., Pock, T. & Bischof, H., 2015, (Accepted/In press) British Machine Vision Conference. .

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

Efficient Minimal-Surface Regularization of Perspective Depth Maps in Variational Stereo

Graber, G., Pock, T., Soatto, S. & Balzer, J., 2015, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). ., p. 511-520

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

Open Access
File
Surface reconstruction
Gages
Topology
Geometry

Learning Reaction-Diffusion Models for Image Inpainting

Yu, W., Heber, S. & Pock, T., 2015, (Accepted/In press) Pattern Recognition: 37th German Conference, GCPR 2015, Aachen, Germany, October 7-10, 2015, Proceedings. Springer International Publishing AG , Vol. 9358. p. 356-367

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

Textures

Learning variational models for blind image deconvolution

Kobler, E., 22 Oct 2015, 134 p.

Research output: ThesisMaster's ThesisResearch

File
Deconvolution
Atmospheric turbulence
Smartphones
Optical transfer function
Neural networks

On learning optimized reaction diffusion processes for effective image restoration

Chen, Y., Yu, W. & Pock, T., 2015, (Accepted/In press) International Conference on Computer Vision. .

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

Real-time flare detection in ground-based Ha imaging at Kanzelhöhe Observatory

Pötzi, W., Veronig, A. M., Riegler, G., Amerstorfer, U., Pock, T., Temmer, M., Polanee, W. & Baumgartner, D. J., 2015, In : Solar physics. 290, 3, p. 951-977

Research output: Contribution to journalArticleResearchpeer-review

Real-time flare detection in ground-based hα imaging at kanzelhhe observatory

Riegler, G. & Pock, T., 2015, In : Solar physics. 290, p. 951-977

Research output: Contribution to journalArticleResearchpeer-review

Vertebrae Segmentation in 3D CT Images based on a Variational Framework

Hammernik, K., Ebner, T., Stern, D., Urschler, M. & Pock, T., 2015, Recent Advances in Computational Methods and Clinical Applications for Spine Imaging and Clinical Applications for Spine Imaging: Part VI. Yao, J., Glocker, B., Klinder, T. & Li, S. (eds.). Switzerland: Springer International Publishing AG , p. 227-233 (Lecture Notes in Computational Vision and Biomechanics; vol. 20).

Research output: Chapter in Book/Report/Conference proceedingChapterResearch

Medical imaging
Tomography
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

Continuous Optimization
Imaging
Imaging techniques
Optical flows
Scale Function

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

Neural networks
Computer vision
Processing

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
Neural networks
Linear programming
Support vector machines
Costs
Processing

Inertial proximal alternating linearized minimization (iPALM) for nonconvex and nonsmooth problems

Pock, T. & Sabach, S., 2016, In : SIAM Journal on Imaging Sciences. 9, 4, p. 1756-1787 32 p.

Research output: Contribution to journalArticleResearchpeer-review

Nonsmooth Function
Non-negative Matrix Factorization
Nonsmooth Optimization
Nonconvex Optimization
Deconvolution

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

Learning a Variational Model for Compressed Sensing MRI Reconstruction

Hammernik, K., Knoll, F., Sodickson, D. & Pock, T., 2016, Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM). 1088

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

Learning Joint Demosaicing and Denoising Based on Sequential Energy Minimization

Klatzer, T., Hammernik, K., Knöbelreiter, P. & Pock, T., 13 May 2016, IEEE International Conference on Computational Photography (ICCP).

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

Open Access
File
Color
Image acquisition
Sensors
Image reconstruction
Image quality

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

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
Image reconstruction
Cameras
Pixels
Optical flows
Image quality

Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization

Shekhovtsov, O., Reinbacher, C., Graber, G. & Pock, T., 23 Jan 2016.

Research output: Contribution to conferencePaperResearchpeer-review

File
Image matching
Optical flows
Computer vision

Total variation on a tree

Kolmogorov, V., Pock, T. & Rolinek, M., 3 May 2016, In : SIAM Journal on Imaging Sciences. 9, 2, p. 605-636 32 p.

Research output: Contribution to journalArticleResearchpeer-review

Total Variation
Dynamic programming
Computer vision
Image processing
Pixels

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

Network architecture
Decoding
Deep learning
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
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., Pock, T., Sodickson, D. K. & Recht, M. P., 2018, p. 3376.

Research output: Contribution to conferenceAbstractResearchpeer-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

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