Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 158 countries worldwide.
Blind Source Separation Using Hessian Evaluation
Jyothirmayi M, Elavaar Kuzhali S, Sethu Selvi S
Pages - 207 - 218     |    Revised - 15-07-2012     |    Published - 10-08-2012
Volume - 6   Issue - 4    |    Publication Date - August 2012  Table of Contents
Blind Source Separation, Sparseness, Hessian Evaluation
This paper focuses on the blind image separation using sparse representation for natural images. The statistics of the natural image is based on one particular statistical property called sparseness, which is closely related to the super-gaussian distribution. Since natural images can have both gaussian and non gaussian distribution, the original infomax algorithm cannot be directly used for source separation as it is better suited to estimate the super-gaussian sources. Hence, we explore the property of sparseness for image representation and show that it can be effectively used for blind source separation. The efficiency of the proposed method is compared with other sparse representation methods through Hessian evaluation.
1 Google Scholar 
2 CiteSeerX 
3 Scribd 
4 SlideShare 
5 PdfSR 
1 Jutten.C, Herault. J.Blind separation of sources part I: “'A adaptive algorithm based on neuromimetic architecture”, Signal Process, ,24:1-10,1991
2 Girolami. M. “An alternative perspective on adaptive independent component analysis algorithms”. Neural Computation, 10 (8) :2103-2114, 1998
3 A. Hyvärinen and E. Oja., A Fast Fixed-Point Algorithm for Independent Component Analysis, Neural Computation, 9(7):1483-1492,1997
4 Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer, Natural Image Statistics-A Probabilistics approach to early computational vision, springer 2009.
5 M. M. Bronstein, A. M. Bronstein, M. Zibulevsky, and Y. Y. Zeevi, “Separation of reflections via sparse ICA,” in Proc. International Conference on Image Processing ICIP, September 2003, vol. 1, pp. 313–316.
6 M. Zibulevsky and B. A. Pearlmutter, “Blind source separation by sparse decomposition in signal dictionary,” Neural Computation,vol. 13, no. 4, pp. 863–882, 2001.
7 Pando Georgiev, Fabian Theis, and Andrzej Cichocki, “Sparse Component Analysis and Blind Source Separation of Underdetermined Mixtures” IEEE Transactions on Neural Networks, VOL. 16, NO. 4, July 2005
8 P Comon. “Independent component analysis –a new concept”.Signal Processing, 36:287-314, 1994.
9 Hyvarinen A. “Independent Component Analysis:Algorithms and Applications”. Neural Networks, 2000, 13:411-430.
10 Bell, A., & Sejnowski, T. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129-1159(1995)
11 Zibulevsky M., Pearlmutter B. A., Boll P., & Kisilev P. (2000) Blind source separation by sparse decomposition in a signal dictionary. In Roberts, S. J. and Everson, R. M. (Eds.),Independent Components Analysis: Principles and Practice, Cambridge University Press
12 Yuanqing Li, Andrzej Cichocki, Shun-ichi Amari, Sergei Shishkin , Sparse Representation and Its Applications in Blind Source Separation NIPS-2003
13 W. Souidene, A. A¨issa-El-Bey, K. Abed-Meriam, and A. Beghdadi, “Blind image separation using sparse presentation,” in Proc. IEEE International Conference on Image Processing ICIP, September 2007, vol. 3, pp. 125–128.
14 Hyvarinen A. “Survey on Independent Component Analysis”. Neural Computing Surveys,1999, 2:94-128.
15 Yuanqing Li, Andrzej Cichocki, Shun-ichi Amari, Sergei Shishkin, Jianting Cao, Fanji Gu,Sparse Representation and Its Applications in Blind Source Separation Seventeenth Annual Conference. on. Neural Information Processing Systems 2003.
16 Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error measurement to structural similarity" IEEE Transactios on Image Processing, vol. 13,no. 1, Jan. 2004
17 V.K.Ananthashayana and Jyothirmayi M, “ Blind Source Separation Using Modified Gaussian Fastica”, International Conference on Signal Processing, Communications and Networking, Sept 2009.
18 J. K. Lin, D. G. Grier, and J. D. Cowan. “Feature extraction approach to blind source separation”.In IEEEWorkshop on Neural Networks for Signal Processing (NNSP), pages 398.405, 1997.
19 T.-W. Lee, M. S. Lewicki, M. Girolami, and T. J. Sejnowski. “Blind source separation of more sources than mixtures using overcomplete representations”. IEEE Signal Processing Letters, 4(5):87.90, 1999.
20 A. Jourjine, S. Rickard, and O. Yilmaz.”Blind separation of disjoint orthogonal signals:Demixing N sources from 2 mixtures”. In IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP2000), volume 5, pages 2985.2988, June 2000.
21 P. Bo_ll and M. Zibulevsky.”Blind separation of more sources than mixtures using the sparsity of the short-time fourier transform”. In 2nd International Workshop on Independent Component Analysis and Blind Signal Separation, pages 87.92, Helsinki, Finland, June19.20, 2000.
22 M. Zibulevsky and B. A. Pearlmutter.”Blind source separation by sparse decomposition in a signal dictionary”,Neural Computation, 13(4):863.882, Apr. 2001
23 Gordon D. Smith, ‘Numerical solution to partial differential equation: finite difference methods’, Oxford University Press, third edition, 2004.
24 J. Karvanen and A. Cichocki, “Measuring sparseness of noisy signals,”in Proc. ICA03,2003.
25 Niall Hurley and Scott Rickard, ”Comparing Measures of sparcity”,in IEEE Transactions on Information Theory , VOL. 55, NO. 10, OCTOBER 2009
26 D. Donoho, M. Elad, and V. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Trans.Inf. Theory, vol. 52, no. 1, pp. 6–18,Jan. 2006.
27 A. Kraskov, H. Stögbauer and P. Grassberger, "Estimating Mutual Information", Physical Review E, vol. 69, pp. 066138, 2004.
28 L. B. Almeida, "Separating a Real-Life Nonlinear Image Mixture", Journal of Machine Learning Research, vol. 6, pp. 1199–1232, 2005.
29 Jyothirmayi M, Elavaar Kuzhali S and Sethu Sevi S, “Blind Image Separation using Forward Difference Method”,Signal and Image Processing :An International Journal,vol2,No 4, pp121-127, Dec2011.
Associate Professor Jyothirmayi M
MSRIT - India
Mr. Elavaar Kuzhali S
MSRIT - India
Professor Sethu Selvi S
MSRIT - India