A Discontinuity Adaptive Method for Super-Resolution of License Plates
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.
KeywordsMarkov Random Field License Plate Intelligent Transport System Blur Kernel License Plate Number
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- 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.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.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.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.Chaudhuri, S.: Super-resolution imaging. Kluwer Academic, USA (2001)Google Scholar
- 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
- 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.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
- 13.Li, S.Z.: Markov random field modeling in computer vision. Springer, Tokyo (1995)Google Scholar
- 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