Pattern Recognition and Image Analysis

, Volume 19, Issue 3, pp 497–500 | Cite as

Video super-resolution with fast deconvolution

  • A. S. KrylovEmail author
  • A. S. Nasonov
  • O. S. Ushmaev
Mathematical Theory of Pattern Recognition


Super-resolution problem is posed as an inverse deconvolution problem. Fast non-iterative super-resolution algorithm based on this approach is suggested. Different super-resolution problem statements for the cases of exactly and inexactly known transform operator were considered.


Adaptive Filter Super Resolution Unsharp Mask Image Super Resolution Super Resolution Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    S. Borman and R. L. Stevenson, “Super-Resolution from Image Sequences—A Review,” Midwest Symposium on Circuits and Systems, pp. 374–378 (1998).Google Scholar
  2. 2.
    A. S. Krylov, A. V. Nasonov, and D. V. Sorokin, “Face Image Super-Resolution from Video Data with Non-Uniform Illumination,” in Proceedings of Int. Conf. Graphicon, 2008, pp. 150–155.Google Scholar
  3. 3.
    S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Fast and Robust Multi-Frame Super-Resolution,” IEEE Trans. On Image Processing 13(10), 1327–1344 (2004).CrossRefGoogle Scholar
  4. 4.
    Sung Won Park and Marios Savvides, “Breaking the Limitation of Manifold Analysis for Super-Resolution of Facial Images,” IEEE Int. Conf. on Acoustics, Speech and Signal Processing 1, 573–576 (2007).Google Scholar
  5. 5.
    Ha V. Le and Guna Seetharaman, “A Super-Resolution Imaging Method Based on Dense Subpixel-Accurate Motion Fields,” in Proceedings of the Third International Workshop on Digital and Computational Video, 2002, pp. 35–42.Google Scholar
  6. 6.
    B. D. Lucas and T. Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision,” in Proceedings of Imaging Understanding Workshop, 1981, pp. 121–130.Google Scholar
  7. 7.
    A. Bruhn, J. Weickert, and C. Shnorr, “Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods,” International Journal of Computer Vision 61(3), 211–231 (2005).CrossRefGoogle Scholar
  8. 8.
    J. Weickert and C. Shnorr, “Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint,” J. Math. Im. and Vis. 14, 245–255 (2001).zbMATHCrossRefGoogle Scholar
  9. 9.
    T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, “High Accuracy Optical Flow Estimation Based on a Theory for Warping,” in Proceedings 8th European Conf. on Computer Vision, 2004, Vol. 4, pp. 25–36.Google Scholar
  10. 10.
    A. N. Tikhonov and V. Y. Arsenin, Solutions of Ill Posed Problems (WH Winston, Washington DC, 1977).zbMATHGoogle Scholar
  11. 11.
    A. S. Lukin, A. S. Krylov, and A. V. Nasonov, “Image Interpolation by Super-Resolution,” in Proceedings of Int. Conf. Graphicon, 2006, pp. 239–242.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2009

Authors and Affiliations

  1. 1.Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and CyberneticsMoscow State UniversityLeninskie gory, MoscowRussia
  2. 2.The Institute of Informatics Problems of the Russian Academy of SciencesMoscowRussia

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