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Simon Setzer

Former Assistant Professor

Position:    Former Assistant Professor

In 2006, I received my diploma in mathematics and computer science at the University of Mannheim, Germany. During my diploma studies, I spend one year at the University of Waterloo, Canada. I defended my Ph.D. thesis in December 2009 at the University of Mannheim. My advisor was Prof. Gabriele Steidl.
From January 2010 to July 2010, I was a research visitor funded by the German Academic Exchange Program DAAD at the group of Prof. Stanley Osher at the University of California, Los Angeles.
Since September 2010, I am a member of the Mathematical Image Analysis Group at the Saarland University.

My research is focused on optimization methods for problems which arise in image processing, computer vision and machine learning. Google scholar profile.

3rd International Workshop on New Computational Methods for Inverse Problems
Institut Farman, Ecole Normale Superieure de Cachan, Cachan, France, May 22, 2013

To Appear

  1. A. Podosinnikova, S. Setzer, M. Hein
    Robust PCA: Optimization of the Robust Reconstruction Error over the Stiefel.
    In X. Jiang, J. Hornegger, R. Koch (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Springer, Berlin, 2014, in press.
    Supplementary material.
  2. N. Persch, C. Schroers, S. Setzer, J. Weickert
    Introducing more physics into variational depth-from-defocus.
    In X. Jiang, J. Hornegger, R. Koch (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Springer, Berlin, 2014, in press.
  3. C. Schroers, S. Setzer, J. Weickert
    A variational taxonomy for surface reconstruction from oriented points.
    Computer Graphics Forum (Proc. 2014 Eurographics Symposium on Geometry Processing), in press.

  4. Journal Publications

  5. T. Goldstein, B. O'Donoghue, S. Setzer, R. Baraniuk
    Fast alternating direction optimization methods.
    SIAM Journal on Imaging Sciences, 7(3), pp. 1588--1623, 2014
  6. S. Setzer, G. Steidl, J. Morgenthaler
    A cyclic projected gradient method.
    Computational Optimization and Applications, 54(2), 417-440, March 2013.
  7. S. Setzer, G. Steidl, T. Teuber
    On vector and matrix median computation.
    Journal of Computational and Applied Mathematics, 236(8), pp. 2200-2222, 2011
  8. S. Setzer
    Operator splittings, Bregman methods and frame shrinkage in image processing.
    International Journal of Computer Vision, 92(3), pp. 265-280, 2011
  9. S. Setzer, G. Steidl, T. Teuber
    Infimal convolution regularizations with discrete l1-type functionals.
    Communications in Mathematical Sciences, 9(3), pp. 797-872, 2011
  10. S. Setzer, G. Steidl, T. Teuber
    Deblurring Poissonian images by split Bregman techniques.
    Journal of Visual Communication and Image Representation, 21, pp. 193 - 199, 2010
  11. S. Setzer, G. Steidl, T. Teuber, G. Moerkotte
    Approximation related to quotient functionals.
    Journal of Approximation Theory, 162(3), pp. 545-558, 2010
  12. S. Didas, S. Setzer, G. Steidl
    Combined l2 data and gradient fitting in conjunction with l1 regularization.
    Advances in Computational Mathematics, 30(1), pp. 79 - 99, 2009
  13. R. H. Chan, S. Setzer, G. Steidl
    Inpainting by flexible Haar wavelet shrinkage.
    SIAM Journal on Imaging Sciences, 1, pp. 273 - 293, 2008
  14. S. Setzer, G. Steidl, T. Teuber
    Restoration of images with rotated shapes.
    Numerical Algorithms, 48, pp. 49 - 66, 2007
  15. G. Steidl, S. Setzer, B. Popilka, B. Burgeth
    Restoration of matrix fields by second order cone programming.
    Computing, 81(2-3), pp. 161-178, 2007
  16. B. Popilka, S. Setzer, G. Steidl
    Signal recovery from incomplete measurements in the presence of outliers.
    Inverse Problems and Imaging, 1(4), pp. 661-672, 2007

  17. Conference Proceedings

  18. M. Hein, S. Setzer, L. Jost, S. Rangapuram
    The total variation on hypergraphs - learning on hypergraphs revisited .
    In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger (Eds.): Advances in Neural Information Processing, (Proc. NIPS 2013, Dec. 5-10, 2013, Lake Tahoe, NE). Vol. 26, 2013. Supplementary material. Code can be found here.
  19. L. Hoeltgen, S. Setzer, J. Weickert
    An optimal control approach to find sparse data for Laplace interpolation .
    In A. Heyden, F. Kahl, C. Olsson, M. Oskarsson, X.-C. Tai (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Vol. 8081, 151-164, Springer, Berlin, 2013.
  20. O. Vogel, K. Hagenburg, J. Weickert, S. Setzer
    A fully discrete theory for linear osmosis filtering .
    In A. Kuijper, T. Pock, K. Bredies, H. Bischof (Eds.): Scale-Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 7893, 368-379, Springer, Berlin, 2013.
  21. T. Bühler, S. Rangapuram, S. Setzer, M. Hein
    Constrained fractional set programs and their application in local clustering and community detection .
    Proceedings of the 30th International Conference on Machine Learning (ICML 2013), JMLR W&CP 28 (1), pp. 624-632, 2013. Supplementary material
  22. M. Hein, S. Setzer
    Beyond spectral clustering: Relaxations of balanced graph cuts.
    Advances in Neural Information Processing Systems (Proc. NIPS, Dec. 2011, Granada, Spain), 24, 2011. Supplementary material
  23. L. Hoeltgen, S. Setzer, M. Breuß
    Intermediate flow field filtering in energy based optic flow computations.
    To appear in Proc. 8th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2011), St. Petersburg, Russia, July 25-27, 2011
  24. S. Setzer
    Split Bregman algorithm, Douglas-Rachford splitting and frame shrinkage.
    In X.-C. Tai et al. (Ed..): Proceedings of the Second International Conference on Scale Space Methods and
    Proceedings of the 2nd International Conference on Scale Space and Variational Methods in Computer Vision, LNCS, vol. 5567, pp. 464-476, Springer, Berlin, 2009

  25. Book Chapters

  26. S. Setzer, G. Steidl, B. Popilka, B. Burgeth
    Variational methods for denoising matrix fields.
    In D. H. Laidlaw and J. Weickert (Eds.): Visualization and Processing of Tensor Fields: Advances and Perspectives, Springer, Berlin, 2009
  27. S. Setzer, G. Steidl
    Variational methods with higher order derivatives in image processing.
    In M. Neamtu and L. L. Schumaker (Eds.): Approximation XII, Nashboro Press, Brentwood, pp. 360 - 386, 2008

  28. Preprints

  29. L. Jost, S. Setzer, M. Hein
    Nonlinear eigenproblems in data analysis - Balanced graph cuts and the RatioDCA-Prox.
    Preprint arXiv:1312.5192, 2013
  30. T. Goldstein, S. Setzer
    High-order methods for basis pursuit.
    Preprint University of California, Los Angeles, 2010. Code can be found here.

  • Journal of Mathematical Imaging and Vision
  • International Journal of Computer Vision
  • Journal of Fourier Analysis and Applications
  • Journal of Computational Physics
  • Journal of Visual Communication and Image Representation
  • Optical Engineering
  • ICCV 2011
  • EMMCVPR 2011, 2013
  • Computational Statistics and Data Analysis
  • SIAM Journal on Imaging Sciences
  • Multiscale Modeling and Simulation
  • Journal of Applied Mathematics and Computing
  • Netherlands Organisation for Scientific Research (NWO)
  • Scale Space and Variational Methods 2013
  • COLT 2013
  • NIPS 2014
  • Advances in Computational Mathematics

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