Publications

Submitted preprints to refereed journals

  • A. Langer and S. Behnamian, DeepTV: A neural network approach for total variation minimization, arXiv preprint ArXiv:2409.05569, 2024. [link] [software]
  • S. Hilb and A. Langer, A General Decomposition Method for a Convex Problem Related to Total Variation Minimization, arXiv preprint ArXiv:2211.00101, 2022. [pdf] [link] [software]

Book chapter

  • A. Langer, Domain Decomposition for Non-smooth (in Particular TV) Minimization. In: Chen K., Schönlieb CB., Tai XC., Younces L. (eds) Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. Springer, Cham, 2021. https://doi.org/10.1007/978-3-030-03009-4_104-1 [pdf]

Refereed journal papers

  • M. Alkämper, S. Hilb, and A. Langer, A primal-dual adaptive finite element method for total variation minimization, Advances in Computational Mathematics, Vol. 51, No. 42, 2025. [link] [software]
  • T. Jacumin and A. Langer, An adaptive finite difference method for total variation minimization, Numerical Algorithms (2025) [link] [software]
  • S. Hilb, A. Langer, and M. Alkämper, A primal-dual finite element method for scalar and vectorial total variation minimization, Journal of Scientific Computing, Vol. 96, No. 24, 2023. [pdf] [link] [software]
  • A. Langer and F. Gaspoz, Overlapping domain decomposition methods for total variation denoising, SIAM Journal on Numerical Analysis, Vol. 57, No. 3, 2019, 1411-1444 [pdf] [link]
  • A. Langer, Locally adaptive total variation for removing mixed Gaussian-impulse noise, International Journal of Computer Mathematics, Vol. 96, No. 2, 2019, 298-316 [link]
  • A. Langer, Investigating the influence of box-constraints on the solution of a total variation model via an efficient primal-dual method, J. Imaging, Vol. 4, No. 1, 2018, 12 (34 pp.) [link]
  • M. Hintermüller, C. N. Rautenberg, T. Wu, and A. Langer, Optimal Selection of the Regularization Function in a Generalized Total Variation Model. Part II: Algorithm, its Analysis and Numerical Tests, Journal of Mathematical Imaging and Vision, 2017, pp. 1-19 [link]
  • A. Langer, Automated parameter selection in the L1-L2-TV model for removing Gaussian plus impulse noise, Inverse Problems, Vol. 33, No. 7, 2017, 074002 (41 pp.) [pdf] [link]
  • M. Alkämper and A. Langer, Using DUNE-ACFem for non-smooth minimization of bounded variation functions, Archive of Numerical Software, Vol. 5, No. 1, 2017, pp. 3-19 [pdf]
  • A. Langer, Automated parameter selection for total variation minimization in image restoration, Journal of Mathematical Imaging and Vision, Vol. 57, No. 2, 2017, pp. 239–268 [pdf] [link]
  • M. Hintermüller and A. Langer, Non-overlapping domain decomposition methods for dual total variation based image denoising , J. Scientific Computing, Vol. 62, No. 2, 2015 pp. 456-481. [pdf]
  • M. Hintermüller and A. Langer, Subspace correction methods for a class of non-smooth and non-additive convex variational problems with mixed L1/L2 data-fidelity in image processing, SIAM J. Imaging Sciences, Vol. 6, No. 4, 2013, pp. 2134-2173 [pdf]
  • A. Langer, S. Osher, and C.-B. Schönlieb, Bregmanized domain decomposition for image restoration, J. Scientific Computing, Vol. 54, 2013, pp. 549-576 [pdf]
  • M. Fornasier, Y. Kim, A. Langer, and C.-B. Schönlieb, Wavelet decomposition method for L2/TV-image deblurring, SIAM J. Imaging Sciences, Vol. 5, No. 3, 2012, pp. 857-885 [pdf]
  • M. Fornasier, A. Langer, and C.-B. Schönlieb, A convergent overlapping domain decomposition method for total variation minimization , Numer. Math., Vol. 116, No. 4, 2010, pp. 645-685 [pdf] [software]

Conference papers

  • S. Behnamian, R. Khaksarinezhad, and A. Langer, FractalPINN-Flow: A Fractal-Inspired Network for Unsupervised Optical Flow Estimation with Total Variation Regularization, In Proceedings of the 2nd Sorbonne-Heidelberg Workshop on AI in Medicine: Machine Learning for Multi-modal Data, 2025 [link] [pdf]
  • M. Hintermüller, A. Langer, C. N. Rautenberg, and T. Wu, Adaptive regularization for image reconstruction from subsampled data, In Proceedings of the International Conference on Imaging, Vision and Learning Based Optimization and PDEs. ILVOPDE 2016. Pages 3–26, Springer, 2018 [pdf]
  • G. Langer, A. Langer, B. Buchegger, J. Jacak, T. A. Klar, and T. Berer, Frequency domain optical resolution photoacoustic and fluorescence microscopy using a modulated laser diode, Proc. SPIE 10064, Photons Plus Ultrasound: Imaging and Sensing 2017, 1006426, 2017. [link]
  • M. Hintermüller and A. Langer, Surrogate functional based subspace correction methods for image processing, In Domain Decomposition Methods in Science and Engineering XXI, pages 829–837. Springer, 2014.[pdf]
  • M. Fornasier, A. Langer, and C.-B. Schönlieb, Domain decomposition methods for compressed sensing , Proc. Int. Conf. SampTA09, Marseilles, 2009. [pdf] or arXiv:0902.0124v1 [math.NA]

Miscellaneous

  • A. Langer, Efficient Numerical Methods for Total Variation Minimization, Habilitation, University of Stuttgart, 2020 [pdf]
  • M. Hintermüller and A. Langer, Adaptive Regularization for Parseval Frames in Image Processing, SFB-Report No. 2014-014 p. 12, 2014 [pdf]
  • A. Langer, Subspace Correction and Domain Decomposition Methods for Total Variation Minimization, Doctoral thesis, Johannes Kepler University of Linz, July 2011. [pdf]
  • A. Langer, Fliterapproximation in der Signalverarbeitung (Approximation of Filters in Signal Processing), Master thesis, Johannes Kepler University of Linz, May 31 2006.