Preprints

• F. Beier, J. von Lindheim, S. Neumayer, G. Steidl (2021).
  Unbalanced Multi-Marginal Optimal Transport.
  (arXiv Preprint #2103.10854)
  [arxiv]
• J. Hertrich, S. Neumayer, G. Steidl (2020).
  Convolutional Proximal Neural Networks and Plug-and-Play Algorithms.
  (arXiv Preprint #2011.02281)
  [arxiv]   [Code]
• P. Koltai, J. von Lindheim, S. Neumayer, G. Steidl (2020).
  Transfer Operators from Optimal Transport Plans for Coherent Set Detection.
  (arXiv Preprint #2006.16085)
  [arxiv]
• S. Neumayer, G. Steidl (2020).
  From Optimal Transport to Discrepancy.
  Accepted in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging.
  [arxiv]

Journal articles

• P. Hagemann, S. Neumayer (2021).
  Stabilizing Invertible Neural Networks Using Mixture Models.
  Inverse Problems.
  [doi]   [Code]
• M. Ehler,  M. Gräf, S. Neumayer, G. Steidl (2021).
  Curve Based Approximation of Measures on Manifolds by Discrepancy Minimization.
  Foundations of Computational Mathematics.
  [doi]
• C. Hartman, H. A. Weiss, P. Lechner, W. Volk, S. Neumayer, J. H. Fitschen, G. Steidl (2021).
  Measurement of strain, strain rate and crack evolution in shear cutting.
  Journal of Materials Processing Technology. 288:116872.
  [doi]
• M. Hasannasab, J. Hertrich, S. Neumayer, G. Plonka, S. Setzer, G. Steidl (2020).
  Parseval Proximal Neural Networks.
  Journal of Fourier Analysis and Applications. 26:59.
  [doi]   [Code]

• A. Effland, S. Neumayer, M. Rumpf (2020).
  Convergence of the Time Discrete Metamorphosis Model on Hadamard Manifolds.
  SIAM Journal on Imaging Sciences. 13(2):557–588.
  [doi]
• M. Bačák, J. Hertrich, S. Neumayer, G. Steidl (2020).
  Minimal Lipschitz and ∞-Harmonic Extensions of Vector-Valued Functions on Finite Graphs.
  Information and Inference: A Journal of the IMA. 9(4):935-959.
  [doi]   [Code]
• S. Neumayer,  M. Nimmer, S. Setzer, G. Steidl (2020).
  On the rotational invariant L₁-norm PCA.
  Linear Algebra and its Applications. 587:243-270.
  [doi]
• S. Neumayer,  M. Nimmer, S. Setzer, G. Steidl (2020).
  On the robust PCA and Weiszfeld's algorithm.
  Applied Mathematics and Optimization. 82:1017-1048.
  [doi]
• L. Lang, S. Neumayer, O. Öktem, C. B. Schönlieb (2020).
  Template-Based Image Reconstruction from Sparse Tomographic Data.
  Applied Mathematics and Optimization. 82:1081-1109.
  [doi]   [Code]
• S. Neumayer, J. Persch, G. Steidl (2019).
  Regularization of Inverse Problems via Time Discrete Geodesics in Image Spaces.
  Inverse Problems. 35(5):055005.
  [doi]
• S. Neumayer, J. Persch, G. Steidl (2018).
  Morphing of Manifold-Valued Images inspired by Discrete Geodesics in Image Spaces.
  SIAM Journal on Imaging Sciences. 11(3):1898–1930.
  [doi]

Conference Proceedings

• J. Lellmann, S. Neumayer, M. Nimmer, G. Steidl, (2019).
  Methods for finding the offset in robust subspace fitting.
  PAMM. 19(1).
  [doi]
• J. Hertrich, M. Bačák, S. Neumayer, G. Steidl (2019).
  Minimal Lipschitz Extensions for Vector-Valued Functions on Finite Graphs.
  Scale Space and Variational Methods in Computer Vision. Lellmann J., Burger M., Modersitzki J. (eds.) Lecture Notes in Computer Science 11603, pages 183-195.
  [doi]   [Code]
• S. Neumayer, M. Nimmer, G. Steidl, H. Stephani (2017).
  On a projected Weizfeld algorithm.
  Scale Space and Variational Methods in Computer Vision. Lauze F., Dong Y., Dahl A. (eds.) Lecture Notes in Computer Science 10302, pages 486-497.
  [doi]