A discontinuity adaptive method for super-resolution of license plates

Authors:
K. V. Suresh;A. N. Rajagopalan
Affiliations:
Image Processing and Computer Vision Laboratory, Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India;Image Processing and Computer Vision Laboratory, Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India
Venue:
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Year:
2006

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Abstract

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.