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2020 – today
- 2024
[j53]Stefan Herdy, Emilio Rodríguez-Caballero, Thomas Pock, Bettina Weber:
Utilization of deep learning tools to map and monitor biological soil crusts. Ecol. Informatics 79: 102417 (2024)- [j52]Martin Zach, Erich Kobler, Antonin Chambolle, Thomas Pock:
Product of Gaussian Mixture Diffusion Models. J. Math. Imaging Vis. 66(4): 504-528 (2024) - [j51]Dominik Narnhofer
, Andreas Habring, Martin Holler, Thomas Pock:
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging. SIAM J. Imaging Sci. 17(1): 301-333 (2024) - [j50]Muhamed Kuric, Jan Ahmetspahic, Thomas Pock:
Total Generalized Variation on a Tree. SIAM J. Imaging Sci. 17(2): 1040-1077 (2024) - [j49]Andreas Habring, Martin Holler, Thomas Pock:
Subgradient Langevin Methods for Sampling from Nonsmooth Potentials. SIAM J. Math. Data Sci. 6(4): 897-925 (2024) - [c102]Edi Muskardin, Martin Tappler, Ingo Pill, Bernhard K. Aichernig, Thomas Pock:
On the Relationship Between RNN Hidden-State Vectors and Semantic Structures. ACL (Findings) 2024: 5641-5658 - [c101]Filip Ilic, He Zhao, Thomas Pock, Richard P. Wildes:
Selective, Interpretable and Motion Consistent Privacy Attribute Obfuscation for Action Recognition. CVPR 2024: 18730-18739 - [c100]Robert Harb, Thomas Pock, Heimo Müller:
Diffusion-based generation of Histopathological Whole Slide Images at a Gigapixel scale. WACV 2024: 5119-5128 - [i58]Filip Ilic, He Zhao, Thomas Pock, Richard P. Wildes:
Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition. CoRR abs/2403.12710 (2024) - [i57]Lea Bogensperger, Dominik Narnhofer, Alexander Falk, Konrad Schindler, Thomas Pock:
FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms. CoRR abs/2405.18087 (2024) - 2023
- [j48]Martin Zach, Florian Knoll, Thomas Pock:
Stable Deep MRI Reconstruction Using Generative Priors. IEEE Trans. Medical Imaging 42(12): 3817-3832 (2023) - [c99]Lea Bogensperger, Dominik Narnhofer, Filip Ilic, Thomas Pock:
Score-Based Generative Models for Medical Image Segmentation Using Signed Distance Functions. DAGM 2023: 3-17 - [c98]Martin Zach, Thomas Pock, Erich Kobler, Antonin Chambolle:
Explicit Diffusion of Gaussian Mixture Model Based Image Priors. SSVM 2023: 3-15 - [c97]Lea Bogensperger, Antonin Chambolle, Alexander Effland, Thomas Pock:
Learned Discretization Schemes for the Second-Order Total Generalized Variation. SSVM 2023: 484-497 - [c96]Lydia Lindner, Alexander Effland, Filip Ilic, Thomas Pock, Erich Kobler:
Lightweight Video Denoising using Aggregated Shifted Window Attention. WACV 2023: 351-360 - [i56]Martin Zach, Thomas Pock, Erich Kobler, Antonin Chambolle:
Explicit Diffusion of Gaussian Mixture Model Based Image Priors. CoRR abs/2302.08411 (2023) - [i55]Tatiana A. Bubba, Luca Calatroni, Ambra Catozzi, Serena Crisci, Thomas Pock, Monica Pragliola, Siiri Rautio, Danilo Riccio, Andrea Sebastiani:
Bilevel learning of regularization models and their discretization for image deblurring and super-resolution. CoRR abs/2302.10056 (2023) - [i54]Erich Kobler, Thomas Pock:
Learning Gradually Non-convex Image Priors Using Score Matching. CoRR abs/2302.10502 (2023) - [i53]Lea Bogensperger, Dominik Narnhofer, Filip Ilic, Thomas Pock:
Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functions. CoRR abs/2303.05966 (2023) - [i52]Lea Bogensperger, Antonin Chambolle, Alexander Effland, Thomas Pock:
Learned Discretization Schemes for the Second-Order Total Generalized Variation. CoRR abs/2303.09349 (2023) - [i51]Tim Tsz-Kit Lau, Han Liu, Thomas Pock:
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms. CoRR abs/2305.15988 (2023) - [i50]Edi Muskardin, Martin Tappler, Ingo Pill, Bernhard K. Aichernig, Thomas Pock:
On the Relationship Between RNN Hidden State Vectors and Semantic Ground Truth. CoRR abs/2306.16854 (2023) - [i49]Robert Harb, Thomas Pock, Heimo Müller:
Diffusion-based generation of Histopathological Whole Slide Images at a Gigapixel scale. CoRR abs/2311.08199 (2023) - 2022
- [j47]Erich Kobler, Alexander Effland, Karl Kunisch, Thomas Pock:
Total Deep Variation: A Stable Regularization Method for Inverse Problems. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9163-9180 (2022) - [j46]Lea Bogensperger, Antonin Chambolle, Thomas Pock:
Convergence of a Piggyback-Style Method for the Differentiation of Solutions of Standard Saddle-Point Problems. SIAM J. Math. Data Sci. 4(3): 1003-1030 (2022) - [j45]Dominik Narnhofer, Alexander Effland, Erich Kobler, Kerstin Hammernik, Florian Knoll, Thomas Pock:
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction. IEEE Trans. Medical Imaging 41(2): 279-291 (2022) - [c95]Thomas Pinetz, Erich Kobler, Thomas Pock, Alexander Effland:
Blind Single Image Super-Resolution via Iterated Shared Prior Learning. GCPR 2022: 151-165 - [c94]Filip Ilic, Thomas Pock, Richard P. Wildes:
Is Appearance Free Action Recognition Possible? ECCV (4) 2022: 156-173 - [c93]Markus Hofinger, Erich Kobler, Alexander Effland, Thomas Pock:
Learned Variational Video Color Propagation. ECCV (23) 2022: 512-530 - [i48]Martin Zach, Erich Kobler, Thomas Pock:
Computed Tomography Reconstruction using Generative Energy-Based Priors. CoRR abs/2203.12658 (2022) - [i47]Filip Ilic, Thomas Pock, Richard P. Wildes:
Is Appearance Free Action Recognition Possible? CoRR abs/2207.06261 (2022) - [i46]Martin Zach, Florian Knoll, Thomas Pock:
Stable deep MRI reconstruction using Generative Priors. CoRR abs/2210.13834 (2022) - [i45]Dominik Narnhofer, Andreas Habring, Martin Holler, Thomas Pock:
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging. CoRR abs/2212.12499 (2022) - 2021
- [j44]Patrick Knöbelreiter, Thomas Pock:
Learned Collaborative Stereo Refinement. Int. J. Comput. Vis. 129(9): 2565-2582 (2021) - [j43]Alexander Effland, Erich Kobler, Thomas Pock, Marko Rajkovic, Martin Rumpf:
Image Morphing in Deep Feature Spaces: Theory and Applications. J. Math. Imaging Vis. 63(2): 309-327 (2021) - [j42]Karli Gillette, Matthias A. F. Gsell, Anton J. Prassl, Elias Karabelas, Ursula Reiter, Gert Reiter, Thomas Grandits, Christian Payer, Darko Stern, Martin Urschler, Jason D. Bayer, Christoph M. Augustin, Aurel Neic, Thomas Pock, Edward J. Vigmond, Gernot Plank:
A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs. Medical Image Anal. 71: 102080 (2021) - [j41]Antonin Chambolle, Thomas Pock:
Learning Consistent Discretizations of the Total Variation. SIAM J. Imaging Sci. 14(2): 778-813 (2021) - [j40]Thomas Pinetz, Erich Kobler, Thomas Pock, Alexander Effland:
Shared Prior Learning of Energy-Based Models for Image Reconstruction. SIAM J. Imaging Sci. 14(4): 1706-1748 (2021) - [c92]Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause:
Learning Atrial Fiber Orientations and Conductivity Tensors from Intracardiac Maps Using Physics-Informed Neural Networks. FIMH 2021: 650-658 - [c91]Vladimir Kolmogorov, Thomas Pock:
One-sided Frank-Wolfe algorithms for saddle problems. ICML 2021: 5665-5675 - [i44]Dominik Narnhofer, Alexander Effland, Erich Kobler, Kerstin Hammernik, Florian Knoll, Thomas Pock:
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction. CoRR abs/2102.06665 (2021) - [i43]Thomas Grandits, Alexander Effland, Thomas Pock, Rolf Krause, Gernot Plank, Simone Pezzuto:
GEASI: Geodesic-based Earliest Activation Sites Identification in cardiac models. CoRR abs/2102.09962 (2021) - [i42]Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause:
Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks. CoRR abs/2102.10863 (2021) - 2020
- [j39]Katrin Lasinger, Christoph Vogel, Thomas Pock, Konrad Schindler:
3D Fluid Flow Estimation with Integrated Particle Reconstruction. Int. J. Comput. Vis. 128(4): 1012-1027 (2020) - [j38]Thomas Grandits, Karli Gillette, Aurel Neic, Jason D. Bayer, Edward J. Vigmond, Thomas Pock, Gernot Plank:
An inverse Eikonal method for identifying ventricular activation sequences from epicardial activation maps. J. Comput. Phys. 419: 109700 (2020) - [j37]Joan Bruna, Eldad Haber, Gitta Kutyniok, Thomas Pock, René Vidal:
Special Issue on the Mathematical Foundations of Deep Learning in Imaging Science. J. Math. Imaging Vis. 62(3): 277-278 (2020) - [j36]Alexander Effland, Erich Kobler, Karl Kunisch, Thomas Pock:
Variational Networks: An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration. J. Math. Imaging Vis. 62(3): 396-416 (2020) - [j35]Antonin Chambolle, Martin Holler, Thomas Pock:
A Convex Variational Model for Learning Convolutional Image Atoms from Incomplete Data. J. Math. Imaging Vis. 62(3): 417-444 (2020) - [j34]Antonin Chambolle, Thomas Pock:
Crouzeix-Raviart Approximation of the Total Variation on Simplicial Meshes. J. Math. Imaging Vis. 62(6-7): 872-899 (2020) - [j33]Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock, Shoham Sabach:
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Nonconvex Optimization. SIAM J. Math. Data Sci. 2(3): 658-682 (2020) - [j32]Florian Knoll, Kerstin Hammernik, Chi Zhang, Steen Moeller, Thomas Pock, Daniel K. Sodickson, Mehmet Akçakaya:
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues. IEEE Signal Process. Mag. 37(1): 128-140 (2020) - [c90]Christian Sormann, Patrick Knöbelreiter, Andreas Kuhn, Mattia Rossi, Thomas Pock, Friedrich Fraundorfer:
BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo. 3DV 2020: 394-403 - [c89]Erich Kobler, Alexander Effland, Karl Kunisch, Thomas Pock:
Total Deep Variation for Linear Inverse Problems. CVPR 2020: 7546-7555 - [c88]Patrick Knöbelreiter, Christian Sormann, Alexander Shekhovtsov, Friedrich Fraundorfer, Thomas Pock:
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems. CVPR 2020: 7897-7906 - [c87]Christian Kopf, Thomas Pock, Bernhard Blaschitz, Svorad Stolc:
Inline Double Layer Depth Estimation with Transparent Materials. GCPR 2020: 418-431 - [c86]Markus Hofinger, Samuel Rota Bulò, Lorenzo Porzi, Arno Knapitsch, Thomas Pock, Peter Kontschieder:
Improving Optical Flow on a Pyramid Level. ECCV (28) 2020: 770-786 - [c85]Thomas Grandits, Simone Pezzuto, Jolijn M. Lubrecht, Thomas Pock, Gernot Plank, Rolf Krause:
PIEMAP: Personalized Inverse Eikonal Model from Cardiac Electro-Anatomical Maps. M&Ms and EMIDEC/STACOM@MICCAI 2020: 76-86 - [i41]Erich Kobler, Alexander Effland, Karl Kunisch, Thomas Pock:
Total Deep Variation for Linear Inverse Problems. CoRR abs/2001.05005 (2020) - [i40]Patrick Knöbelreiter, Christian Sormann, Alexander Shekhovtsov, Friedrich Fraundorfer, Thomas Pock:
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems. CoRR abs/2003.06258 (2020) - [i39]Erich Kobler, Alexander Effland, Karl Kunisch, Thomas Pock:
Total Deep Variation: A Stable Regularizer for Inverse Problems. CoRR abs/2006.08789 (2020) - [i38]Christian Sormann, Patrick Knöbelreiter, Andreas Kuhn, Mattia Rossi, Thomas Pock, Friedrich Fraundorfer:
BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo. CoRR abs/2010.12436 (2020) - [i37]Thomas Pinetz, Erich Kobler, Thomas Pock, Alexander Effland:
Shared Prior Learning of Energy-Based Models for Image Reconstruction. CoRR abs/2011.06539 (2020)
2010 – 2019
- 2019
- [j31]Alexander Effland, Erich Kobler, Anne Brandenburg, Teresa Klatzer, Leonie Neuhäuser, Michael Hölzel, Jennifer Landsberg, Thomas Pock, Martin Rumpf:
Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data. Int. J. Comput. Assist. Radiol. Surg. 14(4): 587-599 (2019) - [j30]Antonin Chambolle, Thomas Pock:
Total roto-translational variation. Numerische Mathematik 142(3): 611-666 (2019) - [j29]Peter Ochs, Thomas Pock:
Adaptive FISTA for Nonconvex Optimization. SIAM J. Optim. 29(4): 2482-2503 (2019) - [c84]Patrick Knöbelreiter, Thomas Pock:
Learned Collaborative Stereo Refinement. GCPR 2019: 3-17 - [c83]Thomas Pinetz, Daniel Soukup, Thomas Pock:
On the Estimation of the Wasserstein Distance in Generative Models. GCPR 2019: 156-170 - [c82]Patricia M. Johnson, Matthew J. Muckley, Mary Bruno, Erich Kobler, Kerstin Hammernik, Thomas Pock, Florian Knoll:
Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. MLMIR@MICCAI 2019: 71-79 - [c81]Senanayak Sesh Kumar Karri, Francis R. Bach, Thomas Pock:
Fast Decomposable Submodular Function Minimization using Constrained Total Variation. NeurIPS 2019: 8183-8193 - [c80]Alexander Effland, Erich Kobler, Thomas Pock, Martin Rumpf:
Time Discrete Geodesics in Deep Feature Spaces for Image Morphing. SSVM 2019: 171-182 - [i36]Florian Knoll, Kerstin Hammernik, Chi Zhang, Steen Moeller, Thomas Pock, Daniel K. Sodickson, Mehmet Akçakaya:
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction. CoRR abs/1904.01112 (2019) - [i35]Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock, Shoham Sabach:
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization. CoRR abs/1904.03537 (2019) - [i34]Senanayak Sesh Kumar Karri, Francis R. Bach, Thomas Pock:
Fast Decomposable Submodular Function Minimization using Constrained Total Variation. CoRR abs/1905.11327 (2019) - [i33]Alexander Effland, Erich Kobler, Karl Kunisch, Thomas Pock:
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration. CoRR abs/1907.08488 (2019) - [i32]Patrick Knöbelreiter, Christoph Vogel, Thomas Pock:
Self-Supervised Learning for Stereo Reconstruction on Aerial Images. CoRR abs/1907.12446 (2019) - [i31]Patrick Knöbelreiter, Thomas Pock:
Learned Collaborative Stereo Refinement. CoRR abs/1907.13391 (2019) - [i30]Thomas Pinetz, Daniel Soukup, Thomas Pock:
On the estimation of the Wasserstein distance in generative models. CoRR abs/1910.00888 (2019) - [i29]Alexander Effland, Erich Kobler, Thomas Pock, Marko Rajkovic, Martin Rumpf:
Image Morphing in Deep Feature Spaces: Theory and Applications. CoRR abs/1910.12672 (2019) - [i28]Markus Hofinger, Samuel Rota Bulò, Lorenzo Porzi, Arno Knapitsch, Thomas Pock, Peter Kontschieder:
The Five Elements of Flow. CoRR abs/1912.10739 (2019) - 2018
- [j28]Gottfried Munda, Christian Reinbacher, Thomas Pock:
Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation. Int. J. Comput. Vis. 126(12): 1381-1393 (2018) - [j27]Doris Antensteiner, Svorad Stolc, Thomas Pock:
A Review of Depth and Normal Fusion Algorithms. Sensors 18(2): 431 (2018) - [j26]Yura Malitsky, Thomas Pock:
A First-Order Primal-Dual Algorithm with Linesearch. SIAM J. Optim. 28(1): 411-432 (2018) - [c79]Christoph Vogel, Patrick Knöbelreiter, Thomas Pock:
Learning Energy Based Inpainting for Optical Flow. ACCV (6) 2018: 340-356 - [c78]Alexander Effland, Michael Hölzel, Teresa Klatzer, Erich Kobler, Jennifer Landsberg, Leonie Neuhäuser, Thomas Pock, Martin Rumpf:
Variational Networks for Joint Image Reconstruction and Classification of Tumor Immune Cell Interactions in Melanoma Tissue Sections. Bildverarbeitung für die Medizin 2018: 334-340 - [c77]Katrin Lasinger, Christoph Vogel, Thomas Pock, Konrad Schindler:
3D Fluid Flow Estimation with Integrated Particle Reconstruction. GCPR 2018: 315-332 - [c76]Erich Kobler, Matthew J. Muckley, Baiyu Chen, Florian Knoll, Kerstin Hammernik, Thomas Pock, Daniel K. Sodickson, Ricardo Otazo:
Variational Deep Learning for Low-Dose Computed Tomography. ICASSP 2018: 6687-6691 - [c75]Doris Antensteiner, Svorad Stolc, Thomas Pock:
Variational Fusion of Light Field and Photometric Stereo for Precise 3D Sensing within a Multi-Line Scan Framework. ICPR 2018: 1036-1042 - [c74]Patrick Knöbelreiter, Christoph Vogel, Thomas Pock:
Self-Supervised Learning for Stereo Reconstruction on Aerial Images. IGARSS 2018: 4379-4382 - [i27]Markus Hofinger, Thomas Pock, Thomas Moosbrugger:
Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow. CoRR abs/1802.04546 (2018) - [i26]Katrin Lasinger, Christoph Vogel, Thomas Pock, Konrad Schindler:
Variational 3D-PIV with Sparse Descriptors. CoRR abs/1804.02872 (2018) - [i25]Katrin Lasinger, Christoph Vogel, Thomas Pock, Konrad Schindler:
3D Fluid Flow Estimation with Integrated Particle Reconstruction. CoRR abs/1804.03037 (2018) - [i24]Christoph Vogel, Patrick Knöbelreiter, Thomas Pock:
Learning Energy Based Inpainting for Optical Flow. CoRR abs/1811.03721 (2018) - 2017
- [j25]Tuomo Valkonen, Thomas Pock:
Acceleration of the PDHGM on Partially Strongly Convex Functions. J. Math. Imaging Vis. 59(3): 394-414 (2017) - [j24]Yunjin Chen, Thomas Pock:
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39(6): 1256-1272 (2017) - [c73]Kerstin Hammernik, Tobias Würfl, Thomas Pock, Andreas K. Maier:
A Deep Learning Architecture for Limited-Angle Computed Tomography Reconstruction. Bildverarbeitung für die Medizin 2017: 92-97 - [c72]Audrey Richard, Christoph Vogel, Maros Blaha, Thomas Pock, Konrad Schindler:
Semantic 3D Reconstruction with Finite Element Bases. BMVC 2017 - [c71]Patrick Knöbelreiter, Christian Reinbacher, Alexander Shekhovtsov, Thomas Pock:
End-to-End Training of Hybrid CNN-CRF Models for Stereo. CVPR 2017: 1456-1465 - [c70]Teresa Klatzer, Daniel Soukup, Erich Kobler, Kerstin Hammernik, Thomas Pock:
Trainable Regularization for Multi-frame Superresolution. GCPR 2017: 90-100 - [c69]Christoph Vogel, Thomas Pock:
A Primal Dual Network for Low-Level Vision Problems. GCPR 2017: 189-202 - [c68]Erich Kobler, Teresa Klatzer, Kerstin Hammernik, Thomas Pock:
Variational Networks: Connecting Variational Methods and Deep Learning. GCPR 2017: 281-293 - [c67]Gottfried Munda, Alexander Shekhovtsov, Patrick Knöbelreiter, Thomas Pock:
Scalable Full Flow with Learned Binary Descriptors. GCPR 2017: 321-332 - [c66]Doris Antensteiner, Svorad Stolc, Kristián Valentín, Bernhard Blaschitz, Reinhold Huber-Mörk, Thomas Pock:
High-Precision 3D Sensing with Hybrid Light Field & Photometric Stereo Approach in Multi-Line Scan Framework. IRIACV 2017: 52-60 - [c65]Thomas Grandits, Thomas Pock:
Optimizing Wavelet Bases for Sparser Representations. EMMCVPR 2017: 249-262 - [c64]Christian Reinbacher, Gottfried Munda, Thomas Pock:
Real-time panoramic tracking for event cameras. ICCP 2017: 106-114 - [c63]Stefan Heber, Wei Yu, Thomas Pock:
Neural EPI-Volume Networks for Shape from Light Field. ICCV 2017: 2271-2279 - [i23]Christian Reinbacher, Gottfried Munda, Thomas Pock:
Real-Time Panoramic Tracking for Event Cameras. CoRR abs/1703.05161 (2017) - [i22]Kerstin Hammernik, Teresa Klatzer, Erich Kobler, Michael P. Recht, Daniel K. Sodickson, Thomas Pock, Florian Knoll:
Learning a Variational Network for Reconstruction of Accelerated MRI Data. CoRR abs/1704.00447 (2017) - [i21]Gottfried Munda, Alexander Shekhovtsov, Patrick Knöbelreiter, Thomas Pock:
Scalable Full Flow with Learned Binary Descriptors. CoRR abs/1707.06427 (2017) - [i20]Audrey Richard, Christoph Vogel, Maros Blaha, Thomas Pock, Konrad Schindler:
Semantic 3D Reconstruction with Finite Element Bases. CoRR abs/1710.01749 (2017) - 2016
- [j23]Antonin Chambolle, Thomas Pock:
An introduction to continuous optimization for imaging. Acta Numer. 25: 161-319 (2016) - [j22]Peter Ochs, René Ranftl, Thomas Brox, Thomas Pock:
Techniques for Gradient-Based Bilevel Optimization with Non-smooth Lower Level Problems. J. Math. Imaging Vis. 56(2): 175-194 (2016) - [j21]Antonin Chambolle, Thomas Pock:
On the ergodic convergence rates of a first-order primal-dual algorithm. Math. Program. 159(1-2): 253-287 (2016) - [j20]Vladimir Kolmogorov, Thomas Pock, Michal Rolínek:
Total Variation on a Tree. SIAM J. Imaging Sci. 9(2): 605-636 (2016) - [j19]Thomas Pock, Shoham Sabach:
Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problems. SIAM J. Imaging Sci. 9(4): 1756-1787 (2016) - [c62]