| Nonconvex TV^q-Models in Image Restoration: Analysis and a Trust-Region Regularization--Based Superlinearly Convergent Solver M Hintermüller, T Wu SIAM Journal on Imaging Sciences 6 (3), 1385-1415, 2013 | 89 | 2013 |
| LED-based photometric stereo: Modeling, calibration and numerical solution Y Quéau, B Durix, T Wu, D Cremers, F Lauze, JD Durou Journal of Mathematical Imaging and Vision 60 (3), 313-340, 2018 | 55 | 2018 |
| A Non-Convex Variational Approach to Photometric Stereo under Inaccurate Lighting Y Queau, T Wu, F Lauze, JD Durou, D Cremers IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 99-108, 2017 | 51 | 2017 |
| Optimal selection of the regularization function in a weighted total variation model. Part II: Algorithm, its analysis and numerical tests M Hintermüller, CN Rautenberg, T Wu, A Langer Journal of Mathematical Imaging and Vision 59 (3), 515-533, 2017 | 38 | 2017 |
| Bilevel Optimization for Calibrating Point Spread Functions in Blind Deconvolution M Hintermüller, T Wu Inverse Problems and Imaging 9 (4), 1139 - 1169, 2015 | 28 | 2015 |
| Semi-Calibrated Near-Light Photometric Stereo Y Quéau, T Wu, D Cremers International Conference on Scale Space and Variational Methods in Computer …, 2017 | 24 | 2017 |
| Robust Principal Component Pursuit via Inexact Alternating Minimization on Matrix Manifolds M Hintermüller, T Wu Journal of Mathematical Imaging and Vision 51 (3), 361-377, 2015 | 21 | 2015 |
| Variational Uncalibrated Photometric Stereo under General Lighting B Haefner, Z Ye, M Gao, T Wu, Y Quéau, D Cremers IEEE International Conference on Computer Vision (ICCV), 8539-8548, 2019 | 18 | 2019 |
| A superlinearly convergent R-regularized Newton scheme for variational models with concave sparsity-promoting priors M Hintermüller, T Wu Computational Optimization and Applications 57 (1), 1-25, 2014 | 15 | 2014 |
| Limiting Aspects of Nonconvex TV^ϕ Models M Hintermüller, T Valkonen, T Wu SIAM Journal on Imaging Sciences 8 (4), 2581-2621, 2015 | 12 | 2015 |
| A Smoothing Descent Method for Nonconvex TV^q-Models M Hintermüller, T Wu Efficient Algorithms for Global Optimization Methods in Computer Vision, 119-133, 2014 | 10 | 2014 |
| Memory-reduction method for pricing American-style options under exponential Lévy processes RH Chan, T Wu East Asian Journal on Applied Mathematics 1 (1), 20-34, 2011 | 8 | 2011 |
| Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform L Ma, J Stückler, T Wu, D Cremers arXiv preprint arXiv:1808.01834, 2018 | 7 | 2018 |
| A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization E Laude, T Wu, D Cremers International Conference on Artificial Intelligence and Statistics (AISTATS …, 2018 | 7 | 2018 |
| Distributed Photometric Bundle Adjustment N Demmel, M Gao, E Laude, T Wu, D Cremers 2020 International Conference on 3D Vision (3DV), 140-149, 2020 | 6 | 2020 |
| Combinatorial Preconditioners for Proximal Algorithms on Graphs T Möllenhoff, Z Ye, T Wu, D Cremers International Conference on Artificial Intelligence and Statistics (AISTATS …, 2018 | 6 | 2018 |
| Optimization of Inf-Convolution Regularized Nonconvex Composite Problems E Laude, T Wu, D Cremers International Conference on Artificial Intelligence and Statistics (AISTATS …, 2019 | 4 | 2019 |
| Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning Z Ye, T Möllenhoff, T Wu, D Cremers International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020 | 2 | 2020 |
| Adaptive Regularization for Image Reconstruction from Subsampled Data M Hintermüller, A Langer, CN Rautenberg, T Wu International Conference on Imaging, Vision and Learning based on …, 2016 | 2 | 2016 |
| Probabilistic Discriminative Learning with Layered Graphical Models Y Shen, T Wu, C Domokos, D Cremers arXiv preprint arXiv:1902.00057, 2019 | 1 | 2019 |
Daniel CremersTechnical University of MunichBestätigte E-Mail-Adresse bei tum.de
