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ORCIDMichael Möller 0001
Michael Moeller 0001
Person information
- affiliation: University of Siegen, Germany
- affiliation: Technical University Munich, Department of Mathematics
- affiliation: University of Münster, Institute for Computational and Applied Mathematics
Other persons with the same name
- Michael Möller 0002 — University of Oldenburg, Department of Computing Science
- Michael Möller 0003 — Adam Ries University of Applied Sciences, Erfurt, Tourism and Regional Marketing
- Michael Möller 0004
![[0000-0001-8713-4420]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-8713-4420")
— Saarland University, Department of Mechatronics, Saarbrücken, Germany (and 3 more) - Michael Möller 0005 — Physikalisch-Technische Bundesanstalt, Berlin, Germany
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2020 – today
- 2024
[c48]Andreas Görlitz, Michael Möller, Andreas Kolb:
Coherent Enhancement of Depth Images and Normal Maps Using Second-Order Geometric Models on Weighted Finite Graphs. 3DV 2024: 623-630- [i54]Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Hannah Dröge, Michael Moeller:
Evaluating Adversarial Robustness of Low dose CT Recovery. CoRR abs/2402.11557 (2024) - [i53]Alexander Auras, Kanchana Vaishnavi Gandikota, Hannah Dröge, Michael Moeller:
Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview. CoRR abs/2402.12072 (2024) - 2023
- [j25]Christian Bauckhage, Wolfgang Förstner, Juergen Gall, Michael Möller, Alexander G. Schwing:
Preface to the Special Issue on Pattern Recognition (DAGM GCPR 2021). Int. J. Comput. Vis. 131(5): 1210 (2023) - [c47]Harshil Bhatia, Edith Tretschk, Zorah Lähner, Marcel Seelbach Benkner, Michael Moeller, Christian Theobalt, Vladislav Golyanik:
CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes. CVPR 2023: 1296-1305 - [c46]Jovita Lukasik, Michael Moeller, Margret Keuper:
An Evaluation of Zero-Cost Proxies - From Neural Architecture Performance Prediction to Model Robustness. DAGM 2023: 624-638 - [c45]Maolin Gao, Paul Roetzer, Marvin Eisenberger, Zorah Lähner, Michael Möller, Daniel Cremers, Florian Bernard:
ΣIGMA: Scale-Invariant Global Sparse Shape Matching. ICCV 2023: 645-654 - [c44]Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk, Zorah Lähner, Michael Moeller, Vladislav Golyanik:
QuAnt: Quantum Annealing with Learnt Couplings. ICLR 2023 - [c43]Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Hannah Dröge, Michael Möller:
Evaluating Adversarial Robustness of Low dose CT Recovery. MIDL 2023: 1545-1563 - [c42]Hannah Dröge, Zorah Lähner, Yuval Bahat, Onofre Martorell Nadal, Felix Heide, Michael Moeller:
Kissing to Find a Match: Efficient Low-Rank Permutation Representation. NeurIPS 2023 - [c41]Christina Runkel, Michael Möller, Carola-Bibiane Schönlieb, Christian Etmann:
Learning Posterior Distributions in Underdetermined Inverse Problems. SSVM 2023: 187-209 - [c40]Zorah Lähner, Michael Moeller:
On the Direct Alignment of Latent Spaces. UniReps 2023: 158-169 - [i52]Harshil Bhatia, Edith Tretschk, Zorah Lähner, Marcel Seelbach Benkner, Michael Moeller, Christian Theobalt, Vladislav Golyanik:
CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes. CoRR abs/2303.16202 (2023) - [i51]Marius Bock, Michael Moeller, Kristof Van Laerhoven, Hilde Kuehne:
WEAR: A Multimodal Dataset for Wearable and Egocentric Video Activity Recognition. CoRR abs/2304.05088 (2023) - [i50]Hendrik Sommerhoff, Shashank Agnihotri, Mohamed Saleh, Michael Moeller, Margret Keuper, Andreas Kolb:
Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters. CoRR abs/2304.14736 (2023) - [i49]Jovita Lukasik, Michael Möller, Margret Keuper:
An Evaluation of Zero-Cost Proxies - from Neural Architecture Performance to Model Robustness. CoRR abs/2307.09365 (2023) - [i48]Maolin Gao, Paul Roetzer, Marvin Eisenberger, Zorah Lähner, Michael Möller, Daniel Cremers, Florian Bernard:
SIGMA: Scale-Invariant Global Sparse Shape Matching. CoRR abs/2308.08393 (2023) - [i47]Hannah Dröge, Zorah Lähner, Yuval Bahat, Onofre Martorell, Felix Heide, Michael Möller:
Kissing to Find a Match: Efficient Low-Rank Permutation Representation. CoRR abs/2308.13252 (2023) - [i46]Marius Bock, Michael Möller, Kristof Van Laerhoven:
Temporal Action Localization for Inertial-based Human Activity Recognition. CoRR abs/2311.15831 (2023) - 2022
- [j24]Marius Bock, Alexander Hoelzemann, Michael Möller, Kristof Van Laerhoven:
Investigating (re)current state-of-the-art in human activity recognition datasets. Frontiers Comput. Sci. 4 (2022) - [j23]Rama Krishna Kandukuri, Jan Achterhold, Michael Möller, Joerg Stueckler:
Physical Representation Learning and Parameter Identification from Video Using Differentiable Physics. Int. J. Comput. Vis. 130(1): 3-16 (2022) - [j22]Paramanand Chandramouli, Kanchana Vaishnavi Gandikota, Andreas Görlitz, Andreas Kolb, Michael Möller:
A Generative Model for Generic Light Field Reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 44(4): 1712-1724 (2022) - [j21]Hartmut Bauermeister, Emanuel Laude, Thomas Möllenhoff, Michael Möller, Daniel Cremers:
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields. SIAM J. Imaging Sci. 15(3): 1253-1281 (2022) - [c39]Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner, Adam Czaplinski, Michael Möller:
A Simple Strategy to Provable Invariance via Orbit Mapping. ACCV (5) 2022: 387-405 - [c38]Hannah Dröge, Yuval Bahat, Felix Heide, Michael Moeller:
Explorable Data Consistent CT Reconstruction. BMVC 2022: 746 - [c37]Lukas Koestler, Daniel Grittner, Michael Möller, Daniel Cremers, Zorah Lähner:
Intrinsic Neural Fields: Learning Functions on Manifolds. ECCV (2) 2022: 622-639 - [c36]Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Michael Möller:
On Adversarial Robustness of Deep Image Deblurring. ICIP 2022: 3161-3165 - [c35]Hannah Dröge, Thomas Möllenhoff, Michael Möller:
Non-Smooth Energy Dissipating Networks. ICIP 2022: 3281-3285 - [c34]Andreas Görlitz, Michael Möller, Andreas Kolb:
FL0C: Fast L0 Cut Pursuit for Estimation of Piecewise Constant Functions. ICIP 2022: 3677-3681 - [c33]Jonas Geiping, Micah Goldblum, Phillip Pope, Michael Moeller, Tom Goldstein:
Stochastic Training is Not Necessary for Generalization. ICLR 2022 - [c32]Tak Ming Wong, Hartmut Bauermeister, Matthias Kahl, Peter Haring Bolívar, Michael Möller, Andreas Kolb:
Deep Optimization Prior for THz Model Parameter Estimation. WACV 2022: 4049-4058 - [i45]Lukas Koestler, Daniel Grittner, Michael Möller, Daniel Cremers, Zorah Lähner:
Intrinsic Neural Fields: Learning Functions on Manifolds. CoRR abs/2203.07967 (2022) - [i44]Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner, Adam Czaplinski, Michael Möller:
A Simple Strategy to Provable Invariance via Orbit Mapping. CoRR abs/2209.11916 (2022) - [i43]Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Michael Moeller:
On Adversarial Robustness of Deep Image Deblurring. CoRR abs/2210.02502 (2022) - [i42]Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk, Zorah Lähner, Michael Moeller, Vladislav Golyanik:
QuAnt: Quantum Annealing with Learnt Couplings. CoRR abs/2210.08114 (2022) - [i41]Samira Kabri, Alexander Auras, Danilo Riccio, Hartmut Bauermeister, Martin Benning, Michael Moeller, Martin Burger:
Convergent Data-driven Regularizations for CT Reconstruction. CoRR abs/2212.07786 (2022) - 2021
- [j20]Hannah Dröge, Baichuan Yuan, Rafael Llerena, Jesse T. Yen, Michael Möller, Andrea L. Bertozzi:
Mitral Valve Segmentation Using Robust Nonnegative Matrix Factorization. J. Imaging 7(10): 213 (2021) - [c31]Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik, Christof Wunderlich, Christian Theobalt, Michael Moeller:
Q-Match: Iterative Shape Matching via Quantum Annealing. ICCV 2021: 7566-7576 - [c30]Hannah Dröge, Michael Möller:
Learning or Modelling? An Analysis of Single Image Segmentation Based on Scribble Information. ICIP 2021: 2274-2278 - [c29]Jonas Geiping, Liam H. Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein:
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. ICLR 2021 - [c28]Marius Bock, Alexander Hölzemann, Michael Moeller, Kristof Van Laerhoven:
Improving Deep Learning for HAR with Shallow LSTMs. ISWC 2021: 7-12 - [i40]Christina Runkel, Christian Etmann, Michael Möller, Carola-Bibiane Schönlieb:
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization. CoRR abs/2102.06496 (2021) - [i39]Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, Tom Goldstein:
What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors. CoRR abs/2102.13624 (2021) - [i38]Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik, Christof Wunderlich, Christian Theobalt, Michael Möller:
Q-Match: Iterative Shape Matching via Quantum Annealing. CoRR abs/2105.02878 (2021) - [i37]Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner, Adam Czaplinski, Michael Möller:
Training or Architecture? How to Incorporate Invariance in Neural Networks. CoRR abs/2106.10044 (2021) - [i36]Marcel Seelbach Benkner, Vladislav Golyanik, Christian Theobalt, Michael Moeller:
Adiabatic Quantum Graph Matching with Permutation Matrix Constraints. CoRR abs/2107.04032 (2021) - [i35]Hartmut Bauermeister, Emanuel Laude, Thomas Möllenhoff, Michael Möller, Daniel Cremers:
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields. CoRR abs/2107.06028 (2021) - [i34]Marius Bock, Alexander Hoelzemann, Michael Moeller, Kristof Van Laerhoven:
Improving Deep Learning for HAR with shallow LSTMs. CoRR abs/2108.00702 (2021) - [i33]Jonas Geiping, Jovita Lukasik, Margret Keuper, Michael Moeller:
DARTS for Inverse Problems: a Study on Hyperparameter Sensitivity. CoRR abs/2108.05647 (2021) - [i32]Jonas Geiping, Micah Goldblum, Phillip E. Pope, Michael Moeller, Tom Goldstein:
Stochastic Training is Not Necessary for Generalization. CoRR abs/2109.14119 (2021) - [i31]Marius Bock, Alexander Hoelzemann, Michael Möller, Kristof Van Laerhoven:
Tutorial on Deep Learning for Human Activity Recognition. CoRR abs/2110.06663 (2021) - 2020
- [j19]Marco Fumero, Michael Möller, Emanuele Rodolà:
Nonlinear spectral geometry processing via the TV transform. ACM Trans. Graph. 39(6): 199:1-199:16 (2020) - [c27]Marcel Seelbach Benkner, Vladislav Golyanik, Christian Theobalt, Michael Moeller:
Adiabatic Quantum Graph Matching with Permutation Matrix Constraints. 3DV 2020: 583-592 - [c26]Jonas Geiping, Fjedor Gaede, Hartmut Bauermeister, Michael Moeller:
Fast Convex Relaxations using Graph Discretizations. BMVC 2020 - [c25]Rama Krishna Kandukuri, Jan Achterhold, Michael Möller, Joerg Stueckler:
Learning to Identify Physical Parameters from Video Using Differentiable Physics. GCPR 2020: 44-57 - [c24]Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein:
Truth or backpropaganda? An empirical investigation of deep learning theory. ICLR 2020 - [c23]Guruprasad M. Hegde, Avinash Nittur Ramesh, Kanchana Vaishnavi Gandikota, Roman Obermaisser, Michael Moeller:
A Simple Domain Shifting Network for Generating Low Quality Images. ICPR 2020: 3963-3968 - [c22]Christina Runkel, Stefan Dorenkamp, Hartmut Bauermeister, Michael Möller:
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction. ICPR 2020: 5246-5253 - [c21]Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, Michael Moeller:
Inverting Gradients - How easy is it to break privacy in federated learning? NeurIPS 2020 - [i30]Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, Michael Moeller:
Inverting Gradients - How easy is it to break privacy in federated learning? CoRR abs/2003.14053 (2020) - [i29]Jonas Geiping, Fjedor Gaede, Hartmut Bauermeister, Michael Moeller:
Fast Convex Relaxations using Graph Discretizations. CoRR abs/2004.11075 (2020) - [i28]Paramanand Chandramouli, Kanchana Vaishnavi Gandikota, Andreas Görlitz, Andreas Kolb, Michael Moeller:
Generative Models for Generic Light Field Reconstruction. CoRR abs/2005.06508 (2020) - [i27]Guruprasad M. Hegde, Avinash Nittur Ramesh, Kanchana Vaishnavi Gandikota, Roman Obermaisser, Michael Moeller:
A Simple Domain Shifting Network for Generating Low Quality Images. CoRR abs/2006.16621 (2020) - [i26]Christina Runkel, Stefan Dorenkamp, Hartmut Bauermeister, Michael Moeller:
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction. CoRR abs/2007.00603 (2020) - [i25]Jonas Geiping, Liam Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein:
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. CoRR abs/2009.02276 (2020) - [i24]Marco Fumero, Michael Möller, Emanuele Rodolà:
Nonlinear Spectral Geometry Processing via the TV Transform. CoRR abs/2009.03044 (2020) - [i23]Rama Krishna Kandukuri, Jan Achterhold, Michael Möller, Jörg Stückler:
Learning to Identify Physical Parameters from Video Using Differentiable Physics. CoRR abs/2009.08292 (2020)
2010 – 2019
- 2019
- [c20]Tak Ming Wong, Matthias Kahl, Peter Haring Bolívar, Andreas Kolb, Michael Möller:
Training Auto-Encoder-Based Optimizers for Terahertz Image Reconstruction. GCPR 2019: 93-106 - [c19]Michael Möller, Thomas Möllenhoff, Daniel Cremers:
Controlling Neural Networks via Energy Dissipation. ICCV 2019: 3255-3264 - [c18]Jonas Geiping, Michael Moeller:
Parametric Majorization for Data-Driven Energy Minimization Methods. ICCV 2019: 10261-10272 - [i22]Michael Möller, Thomas Möllenhoff, Daniel Cremers:
Controlling Neural Networks via Energy Dissipation. CoRR abs/1904.03081 (2019) - [i21]Tak Ming Wong, Matthias Kahl, Peter Haring Bolívar, Andreas Kolb, Michael Möller:
Training Auto-encoder-based Optimizers for Terahertz Image Reconstruction. CoRR abs/1907.01377 (2019) - [i20]Jonas Geiping, Michael Moeller:
Parametric Majorization for Data-Driven Energy Minimization Methods. CoRR abs/1908.06209 (2019) - [i19]Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein:
Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory. CoRR abs/1910.00359 (2019) - 2018
- [j18]Björn Bringmann, Daniel Cremers, Felix Krahmer, Michael Möller:
The homotopy method revisited: Computing solution paths of ℓ1-regularized problems. Math. Comput. 87(313): 2343-2364 (2018) - [j17]Jonas Geiping, Michael Möller:
Composite Optimization by Nonconvex Majorization-Minimization. SIAM J. Imaging Sci. 11(4): 2494-2528 (2018) - [c17]Rania Briq, Michael Moeller, Jürgen Gall:
Convolutional Simplex Projection Network for Weakly Supervised Semantic Segmentation. BMVC 2018: 263 - [c16]Florian Bernard, Christian Theobalt, Michael Möller:
DS*: Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems. CVPR 2018: 4310-4319 - [c15]Peter Ochs, Tim Meinhardt, Laura Leal-Taixé, Michael Möller:
Lifting Layers: Analysis and Applications. ECCV (1) 2018: 53-68 - [c14]Thomas Frerix, Thomas Möllenhoff, Michael Möller, Daniel Cremers:
Proximal Backpropagation. ICLR (Poster) 2018 - [c13]Mai Lan Ha, Gianni Franchi, Michael Möller, Andreas Kolb, Volker Blanz:
Segmentation and Shape Extraction from Convolutional Neural Networks. WACV 2018: 1509-1518 - [i18]Jonas Geiping, Michael Möller:
Composite Optimization by Nonconvex Majorization-Minimization. CoRR abs/1802.07072 (2018) - [i17]Peter Ochs, Tim Meinhardt, Laura Leal-Taixé, Michael Möller:
Lifting Layers: Analysis and Applications. CoRR abs/1803.08660 (2018) - [i16]Michael Moeller, Otmar Loffeld, Juergen Gall, Felix Krahmer:
Are good local minima wide in sparse recovery? CoRR abs/1806.08296 (2018) - [i15]Rania Briq, Michael Moeller, Juergen Gall:
Convolutional Simplex Projection Network (CSPN) for Weakly Supervised Semantic Segmentation. CoRR abs/1807.09169 (2018) - 2017
- [j16]Emanuele Rodolà, Michael Möller, Daniel Cremers:
Regularized Pointwise Map Recovery from Functional Correspondence. Comput. Graph. Forum 36(8): 700-711 (2017) - [c12]Jonas Geiping, Hendrik Dirks, Daniel Cremers, Michael Möller:
Multiframe Motion Coupling for Video Super Resolution. EMMCVPR 2017: 123-138 - [c11]Tim Meinhardt, Michael Möller, Caner Hazirbas, Daniel Cremers:
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems. ICCV 2017: 1799-1808 - [c10]Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger, Daniel Cremers, Guy Gilboa, Carola-Bibiane Schönlieb:
Nonlinear Spectral Image Fusion. SSVM 2017: 41-53 - [i14]Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger, Daniel Cremers, Guy Gilboa, Carola-Bibiane Schönlieb:
Nonlinear Spectral Image Fusion. CoRR abs/1703.08001 (2017) - [i13]Tim Meinhardt, Michael Möller, Caner Hazirbas, Daniel Cremers:
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems. CoRR abs/1704.03488 (2017) - [i12]Thomas Frerix, Thomas Möllenhoff, Michael Möller, Daniel Cremers:
Proximal Backpropagation. CoRR abs/1706.04638 (2017) - [i11]Florian Bernard, Christian Theobalt, Michael Möller:
Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems. CoRR abs/1711.10733 (2017) - 2016
- [j15]Joan Duran, Michael Möller, Catalina Sbert, Daniel Cremers:
On the Implementation of Collaborative TV Regularization: Application to Cartoon+Texture Decomposition. Image Process. Line 6: 27-74 (2016) - [j14]Guy Gilboa, Michael Möller, Martin Burger:
Nonlinear Spectral Analysis via One-Homogeneous Functionals: Overview and Future Prospects. J. Math. Imaging Vis. 56(2): 300-319 (2016) - [j13]Michael Möller, Xiaoqun Zhang:
Fast sparse reconstruction: Greedy inverse scale space flows. Math. Comput. 85(297): 179-208 (2016) - [j12]Joan Duran, Michael Möller, Catalina Sbert, Daniel Cremers:
Collaborative Total Variation: A General Framework for Vectorial TV Models. SIAM J. Imaging Sci. 9(1): 116-151 (2016) - [j11]Martin Burger, Guy Gilboa, Michael Möller, Lina Eckardt, Daniel Cremers:
Spectral Decompositions Using One-Homogeneous Functionals. SIAM J. Imaging Sci. 9(3): 1374-1408 (2016) - [c9]Thomas Möllenhoff, Emanuel Laude, Michael Möller, Jan Lellmann, Daniel Cremers:
Sublabel-Accurate Relaxation of Nonconvex Energies. CVPR 2016: 3948-3956 - [c8]Emanuel Laude, Thomas Möllenhoff, Michael Möller, Jan Lellmann, Daniel Cremers:
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies. ECCV (1) 2016: 614-627 - [i10]Martin Burger, Guy Gilboa, Michael Möller, Lina Eckardt, Daniel Cremers:
Spectral Decompositions using One-Homogeneous Functionals. CoRR abs/1601.02912 (2016) - [i9]Emanuel Laude, Thomas Möllenhoff, Michael Möller, Jan Lellmann, Daniel Cremers:
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies. CoRR abs/1604.01980 (2016) - [i8]Hendrik Dirks, Jonas Geiping, Daniel Cremers, Michael Möller:
Multiframe Motion Coupling via Infimal Convolution Regularization for Video Super Resolution. CoRR abs/1611.07767 (2016) - 2015
- [j10]Thomas Möllenhoff, Evgeny Strekalovskiy, Michael Möller, Daniel Cremers:
The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings. SIAM J. Imaging Sci. 8(2): 827-857 (2015) - [j9]Michael Möller, Martin Benning, Carola Schönlieb, Daniel Cremers:
Variational Depth From Focus Reconstruction. IEEE Trans. Image Process. 24(12): 5369-5378 (2015) - [c7]Michael Möller, Julia Diebold, Guy Gilboa, Daniel Cremers:
Learning Nonlinear Spectral Filters for Color Image Reconstruction. ICCV 2015: 289-297 - [c6]Martin Burger, Lina Eckardt, Guy Gilboa, Michael Möller:
Spectral Representations of One-Homogeneous Functionals. SSVM 2015: 16-27 - [c5]Julia Diebold, Nikolaus Demmel, Caner Hazirbas, Michael Möller, Daniel Cremers:
Interactive Multi-label Segmentation of RGB-D Images. SSVM 2015: 294-306 - [c4]Emanuele Rodolà, Michael Möller, Daniel Cremers:
Point-wise Map Recovery and Refinement from Functional Correspondence. VMV 2015: 25-32 - [i7]Emanuele Rodolà, Michael Möller, Daniel Cremers:
Point-wise Map Recovery and Refinement from Functional Correspondence. CoRR abs/1506.05603 (2015) - [i6]Joan Duran, Michael Möller, Catalina Sbert, Daniel Cremers:
Collaborative Total Variation: A General Framework for Vectorial TV Models. CoRR abs/1508.01308 (2015) - [i5]Guy Gilboa, Michael Möller, Martin Burger:
Nonlinear Spectral Analysis via One-homogeneous Functionals - Overview and Future Prospects. CoRR abs/1510.01077 (2015) - [i4]Thomas Möllenhoff, Emanuel Laude, Michael Möller, Jan Lellmann, Daniel Cremers:
Sublabel-Accurate Relaxation of Nonconvex Energies. CoRR abs/1512.01383 (2015) - 2014
- [j8]Michael Möller, Martin Burger, Peter Dieterich, Albrecht Schwab:
A framework for automated cell tracking in phase contrast microscopic videos based on normal velocities. J. Vis. Commun. Image Represent. 25(2): 396-409 (2014) - [j7]Michael Möller, Eva-Maria Brinkmann, Martin Burger, Tamara Seybold:
Color Bregman TV. SIAM J. Imaging Sci. 7(4): 2771-2806 (2014) - [c3]Thomas Möllenhoff, Evgeny Strekalovskiy, Michael Möller, Daniel Cremers:
Low Rank Priors for Color Image Regularization. EMMCVPR 2014: 126-140 - [c2]Joan Duran, Michael Möller, Catalina Sbert, Daniel Cremers:
A Novel Framework for Nonlocal Vectorial Total Variation Based on ℓ p, q, r -norms. EMMCVPR 2014: 141-154 - [i3]Thomas Möllenhoff, Evgeny Strekalovskiy, Michael Möller, Daniel Cremers:
The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings. CoRR abs/1407.1723 (2014) - [i2]Michael Möller, Martin Benning, Carola-Bibiane Schönlieb, Daniel Cremers:
Variational Depth from Focus Reconstruction. CoRR abs/1408.0173 (2014) - 2013
- [j6]Martin Burger, Michael Möller, Martin Benning, Stanley J. Osher:
An adaptive inverse scale space method for compressed sensing. Math. Comput. 82(281): 269-299 (2013) - [j5]Yi Yang, Michael Möller, Stanley J. Osher:
A dual split Bregman method for fast ℓ1 minimization. Math. Comput. 82(284): 2061-2085 (2013) - [j4]Michael Möller, Martin Burger:
Multiscale Methods for Polyhedral Regularizations. SIAM J. Optim. 23(3): 1424-1456 (2013) - 2012
- [b1]Michael Möller:
Multiscale methods for (generalized) sparse recovery and applications in high dimensional imaging. University of Münster, 2012, pp. 1-260 - [j3]Michael Möller, Todd Wittman, Andrea L. Bertozzi, Martin Burger:
A Variational Approach for Sharpening High Dimensional Images. SIAM J. Imaging Sci. 5(1): 150-178 (2012) - [j2]Ernie Esser, Michael Möller, Stanley J. Osher, Guillermo Sapiro, Jack Xin:
A Convex Model for Nonnegative Matrix Factorization and Dimensionality Reduction on Physical Space. IEEE Trans. Image Process. 21(7): 3239-3252 (2012) - [c1]Michael Möller:
The adaptive inverse scale space method for hyperspectral unmixing. IGARSS 2012: 7492-7495 - [i1]Michael Möller, Martin Burger, Peter Dieterich, Albrecht Schwab:
A Framework for Automated Cell Tracking in Phase Contrast Microscopic Videos based on Normal Velocities. CoRR abs/1203.5914 (2012) - 2010
- [j1]Sheida Rahmani, Melissa Strait, Daria Merkurjev, Michael Möller, Todd Wittman:
An Adaptive IHS Pan-Sharpening Method. IEEE Geosci. Remote. Sens. Lett. 7(4): 746-750 (2010)
Coauthor Index
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Citation data
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load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
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last updated on 2024-06-22 00:22 CEST by the dblp team
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