Journal of Multimedia, Vol 7, No 3 (2012), 247-253, Jun 2012
doi:10.4304/jmm.7.3.247-253

A Novel De-noising Model Based on Independent Component Analysis and Beamlet Transform

Guangming Zhang, Zhiming Cui, Pengpeng Zhao, Jian Wu

Abstract


Vehicle video key frame processing as an important part of intelligent transportation systems plays a significant role. Traditional vehicle video key frame extraction often has lots of noises, it can’t meet the requirements of the recognition and tracking. In this paper, a novel method which is combined independent component analysis with beamlet transform is proposed. Firstly, a random matrix was produce to separate the key frame into a separated image for estimate. Then beamlet transform was applied to optimize the coefficients. At last, the coefficients were selected for image reconstruction by inverse of the beamlet transform. By contrast, this approach could remove more noises and reserve more details, and the efficiency of our approach is better than other traditional de-noising approaches.


Keywords


beamlet transform; independent component analysis; de-noising; video key frame

References


Yiyan Wang, Yuexian Zou, Hang Shi, He Zhao, “Video Image Vehicle Detection System for Signaled Traffic Intersection,” International Conference on Hybrid Intelligent Systems, vol. 1, pp. 222-227, 2009.

P. Comon, “Independent component analysis,” Proceedings of the International Signal Processing Workshop on Higher Order Statistics, Chamrousse, France, 1992, pp 29.

D. L. Donoho and Xiaoming Huo, “Beamlets and Multiscale Image Analysis,” http://www-stat.stanford.edu/~donoho/Reports/2001/BeamletMSIP051101.pdf, 2001.

L. Ying and E. Salari, “Beamlet Transform Based Technique for Pavement Image Processing and Classification,” Proceedings of 2009 IEEE International Conference on Electro/Information Technology, EIT 2009, pp141-145, 2009.

Hyvarinen A, “Independent Component Analysis,” John Wiley and Sons, 2001(5):223 - 225.

C. Jutten, J. Herault, “Independent component analysis verus principal component analysis,” Europ. Signal Processing Conf. (EUSIPC088), Grenoble, France, 1988.

Hyvarinen. A, Cristescu. R, Oja. E, “Fast algorithm for estimating overcomplete ICA bases for image windows,” International Joint Conference on Neural Networks (IJCNN'99), USA, 1999. vol. 2, pp894-899.

Hyvarinen. A, “Fast ICA for noisy data using Gaussian moments,” Proceedings of the 1999 IEEE International Symposium on Circuits and Systems, USA, 1999, vol.5, pp57-61.

D.L. Donoho, “Wedgelets: Nearly minimax estimation of edges,” Annals of Statistics, vol.27(3), pp859–897, 1999.

D. L. Donoho and O Levi, “Fast X-Ray and Beamlet Transforms for Three-D Data,” http://www-stat.stanford.edu/~donoho/Reports/2002/. Three-D-Beamlets.pdf, 2002


Full Text: PDF


Journal of Multimedia (JMM, ISSN 1796-2048)

Copyright @ 2006-2013 by ACADEMY PUBLISHER – All rights reserved.