A Discontinuity Adaptive Method for Super-Resolution of License Plates

  • K. V. Suresh
  • A. N. Rajagopalan
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 
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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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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