A Smoothing Descent Method for Nonconvex TV $$^q$$ -Models. - Michael Hintermüller; Tao Wu 006 (Efficient Algorithms for Global Optimization Methods in Computer Vision, 2011)

Nonconvex TVq-Models in Image Restoration: Analysis and a Trust-Region Regularization-Based Superlinearly Convergent Solver. - Michael Hintermüller; Tao Wu 006 (SIAM J. Imaging Sci., 2013)

A superlinearly convergent R-regularized Newton scheme for variational models with concave sparsity-promoting priors. - Michael Hintermüller; Tao Wu 006 (Comput. Optim. Appl., 2014)

Robust Principal Component Pursuit via Inexact Alternating Minimization on Matrix Manifolds. - Michael Hintermüller; Tao Wu 006 (J. Math. Imaging Vis., 2015)

Limiting Aspects of Nonconvex TVφ Models. - Michael Hintermüller; Tuomo Valkonen; Tao Wu 006 (SIAM J. Imaging Sci., 2015)

A Non-convex Variational Approach to Photometric Stereo under Inaccurate Lighting. - Yvain Quéau; Tao Wu 006; François Lauze; Jean-Denis Durou; Daniel Cremers (CVPR, 2017)

Semi-calibrated Near-Light Photometric Stereo. - Yvain Quéau; Tao Wu 006; Daniel Cremers (SSVM, 2017)

Optimal Selection of the Regularization Function in a Weighted Total Variation Model. Part II: Algorithm, Its Analysis and Numerical Tests. - Michael Hintermüller; Carlos N. Rautenberg; Tao Wu 006; Andreas Langer (J. Math. Imaging Vis., 2017)

A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization. - Emanuel Laude; Tao Wu 006; Daniel Cremers (AISTATS, 2018)

Combinatorial Preconditioners for Proximal Algorithms on Graphs. - Thomas Möllenhoff; Zhenzhang Ye; Tao Wu 006; Daniel Cremers (AISTATS, 2018)

Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform. - Lingni Ma; Jörg Stückler; Tao Wu 006; Daniel Cremers (CoRR, 2018)

LED-Based Photometric Stereo: Modeling, Calibration and Numerical Solution. - Yvain Quéau; Bastien Durix; Tao Wu 006; Daniel Cremers; François Lauze; Jean-Denis Durou (J. Math. Imaging Vis., 2018)

Optimization of Inf-Convolution Regularized Nonconvex Composite Problems. - Emanuel Laude; Tao Wu 006; Daniel Cremers (AISTATS, 2019)

Variational Uncalibrated Photometric Stereo Under General Lighting. - Zhenzhang Ye; Bjoern Haefner; Maolin Gao; Tao Wu 006; Yvain Quéau; Daniel Cremers (ICCV, 2019)

Probabilistic Discriminative Learning with Layered Graphical Models. - Yuesong Shen; Tao Wu 006; Csaba Domokos; Daniel Cremers (CoRR, 2019)

Informative GANs via Structured Regularization of Optimal Transport. - Pierre Bréchet; Tao Wu 006; Thomas Möllenhoff; Daniel Cremers (CoRR, 2019)

Distributed Photometric Bundle Adjustment. - Nikolaus Demmel; Maolin Gao; Emanuel Laude; Tao Wu 006; Daniel Cremers (3DV, 2020)

Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning. - Zhenzhang Ye; Thomas Möllenhoff; Tao Wu 006; Daniel Cremers (AISTATS, 2020)