Abstract
A recognizable license plate in a picture taken by a traffic monitoring system is crucial for identifying the vehicles involved in traffic violations. In the image of a vehicle taken by a surveillance camera, the license plate is often blurred due to fast motion and cannot be recognized by the human eye. In this type of blurring, the blur kernel can be seen to be a linear uniform convolution parametrically described by its angle and length. In this paper, we introduce a new estimation technique to determine this kernel accurately in order to improve our de-blurred result. We use the Hough transform in estimating the direction in which the image is blurred. To determine the extent of the blur in that direction, we employ a new method involving the cepstrum of the blurred image. We compare the performance of our method to that of other recent blind de-blurring techniques. These comparisons show that our proposed scheme can handle significant blur in the captured image to give a good output image.
Similar content being viewed by others
References
Zhou, W., Li, H., Lu, Y., & Tian, Q. (2012). Principle visual word discovery for automatic license plate detection. IEEE Transactions on Image Processing, 21(9), 4269–4279.
Zhou, W., Lu, Y., Li, H., Song, Y., & Tian, Q. (2016). Spatial coding for large scale partial-duplicate Web image search. In Proceedings of the 18th ACM International Conference on Multimedia (pp. 511–520).
Zhou, W., Li, H., Hong, R., Lu, Y., & Tian, Q. (2015). BSIFT: Toward data-independent codebook for large scale image search. IEEE Transactions on Image Processing, 24(3), 967–979.
Zhou, W., Yang, M., Li, H., Wang, X., Lin, Y., & Tian, Q. (2014). Towards codebook-free: Scalable cascaded hashing for mobile image search. IEEE Transactions on Multimedia, 16(3), 601–611.
Cho, S., & Lee, S. (2009). Fast motion de-blurring. ACM Transactions on Graphics, 28(5), 145.
Shan, Q., Jia, J., & Agarwala, A. (2008). High-quality motion de-blurring from a single image. ACM Transactions on Graphics, 27(3), 73.
Xu, L., Zheng, S. & Jia, J. (2013). Unnatural sparse representation for natural image de-blurring. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1107–1114).
Cho, H., Wang, J., & Lee, S. (2012). Text image de-blurring using text-specific properties. In Proceedings of the European conference on computer vision (pp. 524–537).
Xu, L., & Jia, J. (2010). Two-phase kernel estimation for robust motion de-blurring. In Proceedings of the European conference on computer vision (pp. 157–170).
Levin, A., Weiss, Y., Durand, F., & Freeman, W. T. (2011). Understanding blind deconvolution algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12), 2354–2367.
Oliveira, J. P., Figueiredo, M. A. T., & Bioucas-Dias, J. M. (2014). Parametric blur estimation for blind restoration of natural images: Linear motion and out-of-focus. IEEE Transactions on Image Processing, 23(1), 466–477.
Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., & Freeman, W. T. (2006). Removing camera shake from a single photograph. ACM Transactions on Graphics, 25(3), 787–794.
Lu, Q., Zhou, W., Fang, L., & Li, H. (2016). Robust blur kernel estimation for license plate images from fast moving vehicles. IEEE Transactions on Image Processing, 25(5), 2311–2323.
Whyte, O., Sivic, J., Zisserman, A., & Ponce, J. (2012). Non-uniform deblurring for shaken images. International Journal of Computer Vision, 98(2), 168–186.
Gupta, A., Joshi, N., Zitnick, C. L., Cohen, M., & Curless, B. (2010) Single image deblurring using motion density functions. In Proceedings of the 11th European conference on computer vision (pp. 171–184).
Zheng, S., Xu, L., & Jia, J. (2013). Forward motion deblurring. In Proceedings of the IEEE international conference on computer vision (pp. 1465–1472).
Tiwari, S., Shukla, V. P., Singh, A. K., & Biradar, S. R. (2013). Review of motion blur estimation techniques. Journal of Image and Graphics, 1(4), 176–184.
Gonzalez, R. C., & Woods, R. E. (2007). Digital Image Processing. Englewood Cliffs: Prentice Hall.
Hartley, R., & Zisserman, A. (2004). Multiple view geometry in computer vision (2nd ed.). Cambridge: Cambridge University Press.
Krishnan, D., Tay, T., & Fergus, R. (2011). Blind deconvolution using a normalized sparsity measure. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 233–240).
Cai, J. F., Ji, H., Liu, C., & Shen, Z. (2012). Framelet based blind motion deblurring from a single image. IEEE Transactions on Image Processing, 21(2), 562–572.
Chang, C. C., & Lin, C. J. (2016). A library for support vector machines. Available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Oliveira, J. P., Figueiredo, M. A. T., & Bioucas-Dias, J. M. (2007). Blind estimation of motion blur parameters for image deconvolution. In Proceedings of the 3rd Iberian conference on pattern recognition and image analysis (pp. 604–611).
Zeng, N., Zhang, H., Li, Y., Liang, J., & Dobaie, A. M. (2017). Denoising and deblurring gold immunochromatographic strip images via gradient projection algorithms. Journal of Neurocomputing, 247, 165–172.
Zeng, N., Wang, Z., Zhang, H., Liu, W., & Alsaadi, F. E. (2016). Deep belief networks for quantitative analysis of a gold immunochromatographic strip. Journal of Cognitive Computation, 8(4), 684–692.
Zeng, N., Wang, Z., Zineddin, B., Li, Y., Du, M., Xiao, L., et al. (2014). Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach. IEEE Transactions on Medical Imaging, 33(5), 1129–1136.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Rao, P.S.P., Muthu, R.K. A New Parametric Kernel Estimation Technique for License Plate Image De-blurring. 3D Res 8, 22 (2017). https://doi.org/10.1007/s13319-017-0133-z
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13319-017-0133-z