Nonlinear Blind Source Separation Using GA Optimized RBF-ICA and its Application to the Image Noise Removal

Xue Mei Wang

doi:10.4028/www.scientific.net/AMR.393-395.205

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 393-395Nonlinear Blind Source Separation Using GA...

Nonlinear Blind Source Separation Using GA Optimized RBF-ICA and its Application to the Image Noise Removal

Abstract:

Today the blind source separation (BSS) algorithms are widely used to separate independent components in a data set based on its statistical properties. Especially in image data applications, the independent component analysis (ICA) based BSS procedure for image pre-processing has been successfully applied for independent component extraction in order to remove the noise signals mixed into the image data. The contribution of this paper refers to the development of a nonlinear BSS method using the radial basis function (RBF) neural network based ICA algorithm, which was built by adopted some modifications in the linear ICA model. Moreover, genetic algorithm (GA) was used to optimize the RBF neural network to obtain satisfactory nonlinear solve of the nonlinear mixing matrix. In the experiments of this work, the GA optimized nonlinear ICA method and other ICA models were applied for image de-noising. A comparative analysis has showed satisfactory and effective image de-noising results obtained by the presented method.

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Periodical:

Advanced Materials Research (Volumes 393-395)

Pages:

205-208

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.393-395.205

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Online since:

November 2011

Authors:

Xue Mei Wang

Keywords:

De-Noising, GA Optimization, Image Processing, Nonlinear ICA

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References

[1] X. Zhao, R. Jing and M. Gu: J. T. Singhua Univ. (Sci. & Tech. ) Vol. 48 (2008) p.1165.

Google Scholar

[2] A. Widodo and B. Yang: Expert Sys. Appl. Vol. 35 (2008), p.307.

Google Scholar

[3] Z. Li, X. Yan, C. Yuan, J. Zhao and Z. Peng: J. Marine Sci. and Appl. Vol. 10 (2011), p.17.

Google Scholar

[4] M. Belkin and P. Niyogi: Neural Comput. Vol. 15 (2003), p.1373.

Google Scholar

[5] Z. Li, X. Yan, C. Yuan, Z. Peng, and L. Li: Mech. Syst. Signal Process. Vol. 25 (2011), p.2589.

Google Scholar

[6] Z. Li, and X. Yan: Hsi-An Chiao Tung Ta Hsueh Vol. 45 (2011), p.44.

Google Scholar

[7] G. Giannakis, and A. Swami, in: Advanced Signal Processing Alg., Arch. and Implem., Proc. of Soc. of Photo-Opt. Instr. Engr., (SPIE'87), San Diego, CA, Vol. 826 (1987), p.199.

Google Scholar

[8] C. Jutten, and J. Herault: Signal Process. Vol. 24 (1991), p.1.

Google Scholar

[9] E. Sorouchyari: Signal Process Vol. 24 (1991), p.21.

Google Scholar

[10] P. Comon: Signal Process. Vol. 24 (1991), p.11.

Google Scholar

[11] Y. Tan, J. Wang, and M. Zuradam: IEEE Trans. Neural Networks Vol. 12 (2001), p.124.

Google Scholar