A Discontinuity Adaptive Method for Super-Resolution of License Plates

  • K. V. Suresh
  • A. N. Rajagopalan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


In this paper, a super-resolution algorithm tailored to enhance license plate numbers of moving vehicles in real traffic videos is proposed. The algorithm uses the information available from multiple, sub-pixel shifted, and noisy low-resolution observations to reconstruct a high-resolution image of the number plate. The image to be super-resolved is modeled as a Markov random field and is estimated from the low-resolution observations by a graduated non-convexity optimization procedure. To preserve edges in the reconstructed number plate for better readability, a discontinuity adaptive regularizer is proposed. Experimental results are given on several real traffic sequences to demonstrate the edge preserving capability of the proposed method and its robustness to potential errors in motion and blur estimates. The method is computationally efficient as all operations are implemented locally in the image domain.


Markov Random Field License Plate Intelligent Transport System Blur Kernel License Plate Number 


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  1. 1.
    Rajagopalan, A.N., Chellappa, R.: Vehicle detection and tracking in video. In: Proc. of Intl. Conf. on Image Process, vol. 1, pp. 351–354 (2000)Google Scholar
  2. 2.
    Zhang, Y., Zhang, C.: A new algorithm for character segmentation of license Plate. In: Proc. of IEEE Intelligent Vehicles Symp., pp. 106–109 (2003)Google Scholar
  3. 3.
    Cui, Y., Huang, Q.: Character extraction of license plates from video. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 502–507 (1997)Google Scholar
  4. 4.
    Sato, T., Kanade, T., Hughes, E., Smith, M., Satoh, S.: Video OCR: Indexing digital news libraries by recognition of superimposed caption. ACM Multimedia Systems Special Issue on Video Libraries 7, 385–395 (1999)Google Scholar
  5. 5.
    Chaudhuri, S.: Super-resolution imaging. Kluwer Academic, USA (2001)Google Scholar
  6. 6.
    Cortijo, F.J., Villena, S., Molina, R., Katsaggelos, A.: Bayesian super-resolution of text image sequences from low-resolution observations. In: IEEE Intl. Symp. on Signal Process. and its Application, pp. 421–424 (2003)Google Scholar
  7. 7.
    Chaudhuri, S., Taur, D.R.: High-resolution slow-motion sequencing - How to generate a slow-motion sequence from a bit stream. IEEE Signal Process. Mag. 22, 16–24 (2005)CrossRefGoogle Scholar
  8. 8.
    Miravet, C., Rodriguez, F.B.: A hybrid MLP-PNN architecture for fast image super-resolution. In: Intl. Conf. on Neural Information Process, pp. 417–424 (2003)Google Scholar
  9. 9.
    Rajaram, S., Gupta, M.D., Petrovic, N., Huang, T.S.: Learning-based nonparametric image super-resolution. EURASIP Journal on Applied Signal Process (2006)Google Scholar
  10. 10.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Trans. on Pattern Anal. and Mach. Intell. 6, 721–741 (1984)MATHCrossRefGoogle Scholar
  11. 11.
    Hardie, R.C., Barnard, K., Armstrong, E.E.: Joint MAP registration and high-resolution image estimation using a sequence of undersampled images. IEEE Trans. on Image Process. 6, 1621–1632 (1997)CrossRefGoogle Scholar
  12. 12.
    Schultz, R., Stevenson, R.L.: Extraction of high-resolution frames from video sequences. IEEE Trans. on Image Process. 5, 996–1011 (1996)CrossRefGoogle Scholar
  13. 13.
    Li, S.Z.: Markov random field modeling in computer vision. Springer, Tokyo (1995)Google Scholar
  14. 14.
    Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP: Graph. Models and Image Process. 53, 231–239 (1991)CrossRefGoogle Scholar
  15. 15.
    Zomet, A., Peleg, S.: Super-resolution from multiple images having arbitrary mutual motion. In: Chaudhuri, S. (ed.) Super-resolution Imaging, pp. 195–209. Kluwer Academic, Dordrecht (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • K. V. Suresh
    • 1
  • A. N. Rajagopalan
    • 1
  1. 1.Image Processing and Computer Vision Laboratory, Department of Electrical EngineeringIndian Institute of Technology MadrasChennaiIndia

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