Humboldt-Universität zu Berlin
Department of Mathematics
Dr. Tao Wu
Papers
M. Hintermüller, T. Valkonen, and T. Wu, Limiting aspects of non-convex TV^phi models. SIAM J. Imaging Sci., vol. 8, pp. 2581--2621, 2015.
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M. Hintermüller and T. Wu, Bilevel optimization for calibrating point spread functions in blind deconvolution. Inverse Problems and Imaging, vol. 9, pp. 1139--1169, 2015.
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M. Hintermüller and T. Wu, Robust principal component pursuit via inexact alternating minimization on matrix manifolds. J. Math. Imaging Vis., vol. 51, pp. 361--377, 2015.
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M. Hintermüller and T. Wu, A superlinearly convergent R-regularized Newton scheme for variational models with concave sparsity-promoting priors. Comput. Optim. Appl., vol. 57, pp. 1--25, 2014.
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M. Hintermüller and T. Wu, A smoothing descent method for nonconvex TV^q-models. In Efficient Algorithms for Global Optimization Methods in Computer Vision, vol. 8293 of Lecture Notes in Computer Science, pp. 119--133, Springer, 2014.
[link] [pdf]
M. Hintermüller and T. Wu, Nonconvex TV^q-models in image restoration: Analysis and a trust-region regularization-based superlinearly convergent solver. SIAM J. Imaging Sci., vol. 6, pp. 1385--1415, 2013.
[link] [pdf]
T. Wu, Sparse Imaging by Nonconvex and Nonsmooth Minimizations: Analyses and Algorithms. PhD thesis, Karl-Franzens-Universität Graz, 2014.
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