Blind Source Separation Using Modified Gaussian FastICA
This paper addresses the problem of source separation
in images. We propose a FastICA algorithm employing a modified
Gaussian contrast function for the Blind Source Separation.
Experimental result shows that the proposed Modified Gaussian
FastICA is effectively used for Blind Source Separation to obtain
better quality images. In this paper, a comparative study has been
made with other popular existing algorithms. The peak signal to
noise ratio (PSNR) and improved signal to noise ratio (ISNR) are
used as metrics for evaluating the quality of images. The ICA metric
Amari error is also used to measure the quality of separation.
[1] J.Karhunen, A.Hyvarinen, R. Vigario, J.Hurri, and E.Oja, "Applications
of neural blind separation to signal and image processing," in Proc. 1997
IEEE Int. conf. on Acoustic, speech, and Signal Processing
(ICASSP-97), Munich, Germany, April 1997 pp-131-134. ,
[2] E.Oja, J.Karhunen, A.Hyvarinen, R. Vigario and J.Hurri, "Neural
Independent Component Analysis - Approaches and Applications," in
Brain-like Computing and intelligent Information Systems, S.I. amari
and N.Kasabov (Eds.), Springer-Verlag, Singapore,pp.167-188., 1998
[3] S.I.Amari, A. Cichocki, and H.H.Yang, "A new learning algorithm for
blind signal separation," 1996, 8:757-763.
[4] A.Hyvarinen, "Fast and Robust Fixed-point Algorithms for Independent
Component Analysis," IEEE Trans. On Neural networks,vol.10(3),p.p
626-634, 1999
[5] A.J.Bell and T.J.Sejnowski, "An information-maximization approach to
blind sep-aration and blind deconvolution," Neural computation,,
1995,7:1129-1159.
[6] P.comon, "Independent component analysis, a new concept?" Signal
Processing, vol.36, no3, pp.287-314, Apr.1994.
[7] J.F.Cardoso, "Infomax and maximum likelihood for blind source
separation," IEEE signal processing letters, vol.4, April1997,pp. 112-
114.
[8] J.Karhunen, E.oja,L.Wang, R.Vigario, and J.Joutsensalo, " A class of
neural networks for independent component analysis," IEEE trans. On
Neural Networks, Mat1997, vol.8,pp.486-504.
[9] Sergey Kirshner and Barnabàs Pòczos, "ICA and ISA Using Schweizer-
Wolff Measure of Dependence," Proceedings of the 25th International
Conference on machine Learning, Helsinki, Finland,2008.
[10] Aapo Hyvärinen,Juha Karhunen and Erkki Oja, "Independent
Component Analysis", John wiley & sons, Inc 2001
[11] Hyvärinen,A., "Fast and robust fixed-point algorithms for independent
component analysis," IEEE Trans. On Neural Networks, 626-634,1999.
[12] A.Cichocki and A. Amari, "Adaptive Blind signal and Image
Processing", John-wiley and sons,2002.
[13] Junhua Wang, "An Image BSS Algorithm Based on Curvelet
Transform", IEEE Transactrions, ICALIP2008, 2008.
[14] Te-Won Lee, Michael S.Lewicki, "The Generalized Gaussian Mixture
Model Using ICA", IEEE Acoustics,speech and signal processing, vol-
2,pp1161-1164,1998.
[1] J.Karhunen, A.Hyvarinen, R. Vigario, J.Hurri, and E.Oja, "Applications
of neural blind separation to signal and image processing," in Proc. 1997
IEEE Int. conf. on Acoustic, speech, and Signal Processing
(ICASSP-97), Munich, Germany, April 1997 pp-131-134. ,
[2] E.Oja, J.Karhunen, A.Hyvarinen, R. Vigario and J.Hurri, "Neural
Independent Component Analysis - Approaches and Applications," in
Brain-like Computing and intelligent Information Systems, S.I. amari
and N.Kasabov (Eds.), Springer-Verlag, Singapore,pp.167-188., 1998
[3] S.I.Amari, A. Cichocki, and H.H.Yang, "A new learning algorithm for
blind signal separation," 1996, 8:757-763.
[4] A.Hyvarinen, "Fast and Robust Fixed-point Algorithms for Independent
Component Analysis," IEEE Trans. On Neural networks,vol.10(3),p.p
626-634, 1999
[5] A.J.Bell and T.J.Sejnowski, "An information-maximization approach to
blind sep-aration and blind deconvolution," Neural computation,,
1995,7:1129-1159.
[6] P.comon, "Independent component analysis, a new concept?" Signal
Processing, vol.36, no3, pp.287-314, Apr.1994.
[7] J.F.Cardoso, "Infomax and maximum likelihood for blind source
separation," IEEE signal processing letters, vol.4, April1997,pp. 112-
114.
[8] J.Karhunen, E.oja,L.Wang, R.Vigario, and J.Joutsensalo, " A class of
neural networks for independent component analysis," IEEE trans. On
Neural Networks, Mat1997, vol.8,pp.486-504.
[9] Sergey Kirshner and Barnabàs Pòczos, "ICA and ISA Using Schweizer-
Wolff Measure of Dependence," Proceedings of the 25th International
Conference on machine Learning, Helsinki, Finland,2008.
[10] Aapo Hyvärinen,Juha Karhunen and Erkki Oja, "Independent
Component Analysis", John wiley & sons, Inc 2001
[11] Hyvärinen,A., "Fast and robust fixed-point algorithms for independent
component analysis," IEEE Trans. On Neural Networks, 626-634,1999.
[12] A.Cichocki and A. Amari, "Adaptive Blind signal and Image
Processing", John-wiley and sons,2002.
[13] Junhua Wang, "An Image BSS Algorithm Based on Curvelet
Transform", IEEE Transactrions, ICALIP2008, 2008.
[14] Te-Won Lee, Michael S.Lewicki, "The Generalized Gaussian Mixture
Model Using ICA", IEEE Acoustics,speech and signal processing, vol-
2,pp1161-1164,1998.
@article{"International Journal of Information, Control and Computer Sciences:61912", author = "V. K. Ananthashayana and Jyothirmayi M.", title = "Blind Source Separation Using Modified Gaussian FastICA", abstract = "This paper addresses the problem of source separation
in images. We propose a FastICA algorithm employing a modified
Gaussian contrast function for the Blind Source Separation.
Experimental result shows that the proposed Modified Gaussian
FastICA is effectively used for Blind Source Separation to obtain
better quality images. In this paper, a comparative study has been
made with other popular existing algorithms. The peak signal to
noise ratio (PSNR) and improved signal to noise ratio (ISNR) are
used as metrics for evaluating the quality of images. The ICA metric
Amari error is also used to measure the quality of separation.", keywords = "Amari error, Blind Source Separation, Contrast
function, Gaussian function, Independent Component Analysis.", volume = "3", number = "8", pages = "2113-4", }