I. Kopriva and D. Nuzillard, "Non-negative matrix factorization approach to blind image deconvolution," Lect. Notes Comput. Sci. 3889, 966-973 (2006).

[CrossRef]

I. Kopriva, D. J. Garrood, and V. Borjanovic, "Single frame blind image deconvolution by non-negative sparse matrix factorization," Opt. Commun. 266, 456-464 (2006).

[CrossRef]

K. Zhang and L. W. Chan, "An adaptive method for subband decomposition ICA," Neural Comput. 18, 191-223 (2006).

[CrossRef]

K. Zhang and L. W. Chan, "Enhancement of source independence for blind source separation," Lect. Notes Comput. Sci. 3889, 731-738 (2006).

[CrossRef]

Y. Li, S. Amari, A. Cichocki, D. W. C. Ho, and S. Xie, "Underdetermined blind source separation based on sparse representation," IEEE Trans. Signal Process. 54, 423-437 (2006).

[CrossRef]

A. Cichocki, R. Zdunek, and S. Amari, "Csiszár's divergences for non-negative matrix factorization: family of new algorithms," Lect. Notes Comput. Sci. 3889, 32-39 (2006).

[CrossRef]

P. Georgiev, F. Theis, and A. Cichocki, "Sparse component analysis and blind source separation of underdetermined mixtures," IEEE Trans. Neural Netw. 16, 992-996 (2005).

[CrossRef]
[PubMed]

A. Cichocki, "Blind source separation: new tools for extraction of source signals and denoising," in Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, H.Szu, ed., Proc. SPIE 5818, 11-25 (2005).

I. Kopriva, "Single-frame multichannel blind deconvolution by nonnegative matrix factorization with sparseness constraints," Opt. Lett. 30, 3135-3137 (2005).

[CrossRef]
[PubMed]

M. M. Bronstein, A. Bronstein, M. Zibulevsky, and Y. Y. Zeevi, "Blind deconvolution of images using optimal sparse representations," IEEE Trans. Image Process. 14, 726-736 (2005).

[CrossRef]
[PubMed]

M. Numata and N. Hamada, "Image restoration of multichannel blurred images by independent component analysis," in Proceedings of 2004 RISP International Workshop on Nonlinear Circuit and Signal Processing, Hawaii, March 5-7, 2004, pp. 197-200.

I. Kopriva, Q. Du, H. Szu, and W. Wasylkiwskyj, "Independent component analysis approach to image sharpening in the presence of atmospheric turbulence," Opt. Commun. 233, 7-14 (2004).

[CrossRef]

T. Tanaka and A. Cichocki, "Subband decomposition independent component analysis and new performance criteria," in Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing (IEEE, 2004), pp. 541-544.

Y. Li, A. Cichocki, and S. Amari, "Analysis of sparse representation and blind source separation," Neural Comput. 16, 1193-1234 (2004).

[CrossRef]
[PubMed]

P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints," J. Mach. Learn. Res. 5, 1457-1469 (2004).

P. Kisilev, M. Zibulevsky, and Y. Y. Zeevi, "Multiscale framework for blind separation of linearly mixed signals," J. Mach. Learn. Res. 4, 1339-1363 (2003).

J. F. Cardoso, "Dependence, correlation and Gaussianity in independent component analysis," J. Mach. Learn. Res. 4, 1177-1203 (2003).

A. Cichocki and P. Georgiev, "Blind source separationalgorithms with matrix constraints," IEICE Trans. Fundamentals E86-A, 522-531 (2003).

A. Cichocki and S. Amari, Adaptive Blind Signal and Image Processing (Wiley, 2002).

[CrossRef]

M. Zibulevsky, P. Kisilev, Y. Zeevi, and B. Pearlmutter, "Blind source separation via multinode sparse representation," in Advances in Neural Information Processing Systems (Morgan Kaufman, 2002), pp. 185-191.

A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis (Wiley Interscience, 2001).

[CrossRef]

S. Umeyama, "Blind deconvolution of blurred images by use of ICA," Electron. Commun. Jpn. Part III: Fund. Electron. Sci. 84, 1-9 (2001).

D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature 401, 788-791 (1999).

[CrossRef]
[PubMed]

A. Hyvärinen, "Independent component analysis for time-dependent stochastic processes," in Proceedings of the International Conference on Artificial Neural Networks (Springer-Verlag Telos, 1998), pp. 541-546.

C. L. Bryne, "Accelerating the EMML algorithm and related iterative algorithms by rescaled block-iterative methods," IEEE Trans. Image Process. 7, 100-109 (1998).

[CrossRef]

D. Kundur and D. Hatzinakos, "Blind image deconvolution," IEEE Signal Process. Mag. 13, 43-64 (1996).

[CrossRef]

P. Comon, "Independent component analysis, a new concept?" Signal Process. 36, 287-314 (1994).

[CrossRef]

W. M. Lam and J. M. Shapiro, "A class of fast algorithms for the Peano-Hillbert space filling curve," in Proceedings of the IEEE International Conference Image Processing (ICIP-94) (IEEE, 1994), Vol. 1, pp. 638-641.

[CrossRef]

M. V. Wickerhauser, Adapted Wavelet Analysis from Theory to Software (Peters, 1994).

J. F. Cardoso and A. Souloumiac, "Blind beamforming for non-Gaussian signals," IEE Proc. F, Radar Signal Process. 140, 362-370 (1993).

[CrossRef]

R. L. Lagendijk and J. Biemond, Iterative Identification and Restoration of Images (Kluwer Academic, 1991).

[CrossRef]

S. J. Orfanidis, Optimum Signal Processing: An Introduction, 2nd ed. (Macmillan, 1988).

J. G. Daugman, "Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression," IEEE Trans. Acoust., Speech, Signal Process. 36, 1169-1179 (1988).

[CrossRef]

D. R. Brillinger, Time Series Data Analysis and Theory (McGraw-Hill, 1981).

M. B. Priestley, Spectral Analysis and Time Series (Academic, 1981).

L. B. Lucy, "An iterative technique for rectification of observed distribution," Astron. J. 79, 745-754 (1974).

[CrossRef]

A. M. Yaglom, Introduction to the Theory of Stationary Random Functions (Prentice Hall, 1962).

Y. Li, S. Amari, A. Cichocki, D. W. C. Ho, and S. Xie, "Underdetermined blind source separation based on sparse representation," IEEE Trans. Signal Process. 54, 423-437 (2006).

[CrossRef]

A. Cichocki, R. Zdunek, and S. Amari, "Csiszár's divergences for non-negative matrix factorization: family of new algorithms," Lect. Notes Comput. Sci. 3889, 32-39 (2006).

[CrossRef]

Y. Li, A. Cichocki, and S. Amari, "Analysis of sparse representation and blind source separation," Neural Comput. 16, 1193-1234 (2004).

[CrossRef]
[PubMed]

A. Cichocki and S. Amari, Adaptive Blind Signal and Image Processing (Wiley, 2002).

[CrossRef]

M. R. Banham and A. K. Katsaggelos, "Digital image restoration," IEEE Signal Process. Mag. 14, 24-41 (1997).

[CrossRef]

R. L. Lagendijk and J. Biemond, Iterative Identification and Restoration of Images (Kluwer Academic, 1991).

[CrossRef]

I. Kopriva, D. J. Garrood, and V. Borjanovic, "Single frame blind image deconvolution by non-negative sparse matrix factorization," Opt. Commun. 266, 456-464 (2006).

[CrossRef]

D. R. Brillinger, Time Series Data Analysis and Theory (McGraw-Hill, 1981).

M. M. Bronstein, A. Bronstein, M. Zibulevsky, and Y. Y. Zeevi, "Blind deconvolution of images using optimal sparse representations," IEEE Trans. Image Process. 14, 726-736 (2005).

[CrossRef]
[PubMed]

M. M. Bronstein, A. Bronstein, M. Zibulevsky, and Y. Y. Zeevi, "Blind deconvolution of images using optimal sparse representations," IEEE Trans. Image Process. 14, 726-736 (2005).

[CrossRef]
[PubMed]

C. L. Bryne, "Accelerating the EMML algorithm and related iterative algorithms by rescaled block-iterative methods," IEEE Trans. Image Process. 7, 100-109 (1998).

[CrossRef]

J. F. Cardoso, "Dependence, correlation and Gaussianity in independent component analysis," J. Mach. Learn. Res. 4, 1177-1203 (2003).

J. F. Cardoso and A. Souloumiac, "Blind beamforming for non-Gaussian signals," IEE Proc. F, Radar Signal Process. 140, 362-370 (1993).

[CrossRef]

K. Zhang and L. W. Chan, "Enhancement of source independence for blind source separation," Lect. Notes Comput. Sci. 3889, 731-738 (2006).

[CrossRef]

K. Zhang and L. W. Chan, "An adaptive method for subband decomposition ICA," Neural Comput. 18, 191-223 (2006).

[CrossRef]

Y. Li, S. Amari, A. Cichocki, D. W. C. Ho, and S. Xie, "Underdetermined blind source separation based on sparse representation," IEEE Trans. Signal Process. 54, 423-437 (2006).

[CrossRef]

A. Cichocki, R. Zdunek, and S. Amari, "Csiszár's divergences for non-negative matrix factorization: family of new algorithms," Lect. Notes Comput. Sci. 3889, 32-39 (2006).

[CrossRef]

P. Georgiev, F. Theis, and A. Cichocki, "Sparse component analysis and blind source separation of underdetermined mixtures," IEEE Trans. Neural Netw. 16, 992-996 (2005).

[CrossRef]
[PubMed]

A. Cichocki, "Blind source separation: new tools for extraction of source signals and denoising," in Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, H.Szu, ed., Proc. SPIE 5818, 11-25 (2005).

T. Tanaka and A. Cichocki, "Subband decomposition independent component analysis and new performance criteria," in Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing (IEEE, 2004), pp. 541-544.

Y. Li, A. Cichocki, and S. Amari, "Analysis of sparse representation and blind source separation," Neural Comput. 16, 1193-1234 (2004).

[CrossRef]
[PubMed]

A. Cichocki and P. Georgiev, "Blind source separationalgorithms with matrix constraints," IEICE Trans. Fundamentals E86-A, 522-531 (2003).

A. Cichocki and S. Amari, Adaptive Blind Signal and Image Processing (Wiley, 2002).

[CrossRef]

P. Comon, "Independent component analysis, a new concept?" Signal Process. 36, 287-314 (1994).

[CrossRef]

J. G. Daugman, "Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression," IEEE Trans. Acoust., Speech, Signal Process. 36, 1169-1179 (1988).

[CrossRef]

I. Kopriva, Q. Du, H. Szu, and W. Wasylkiwskyj, "Independent component analysis approach to image sharpening in the presence of atmospheric turbulence," Opt. Commun. 233, 7-14 (2004).

[CrossRef]

I. Kopriva, D. J. Garrood, and V. Borjanovic, "Single frame blind image deconvolution by non-negative sparse matrix factorization," Opt. Commun. 266, 456-464 (2006).

[CrossRef]

P. Georgiev, F. Theis, and A. Cichocki, "Sparse component analysis and blind source separation of underdetermined mixtures," IEEE Trans. Neural Netw. 16, 992-996 (2005).

[CrossRef]
[PubMed]

A. Cichocki and P. Georgiev, "Blind source separationalgorithms with matrix constraints," IEICE Trans. Fundamentals E86-A, 522-531 (2003).

M. Numata and N. Hamada, "Image restoration of multichannel blurred images by independent component analysis," in Proceedings of 2004 RISP International Workshop on Nonlinear Circuit and Signal Processing, Hawaii, March 5-7, 2004, pp. 197-200.

D. Kundur and D. Hatzinakos, "Blind image deconvolution," IEEE Signal Process. Mag. 13, 43-64 (1996).

[CrossRef]

Y. Li, S. Amari, A. Cichocki, D. W. C. Ho, and S. Xie, "Underdetermined blind source separation based on sparse representation," IEEE Trans. Signal Process. 54, 423-437 (2006).

[CrossRef]

P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints," J. Mach. Learn. Res. 5, 1457-1469 (2004).

A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis (Wiley Interscience, 2001).

[CrossRef]

A. Hyvärinen, "Independent component analysis for time-dependent stochastic processes," in Proceedings of the International Conference on Artificial Neural Networks (Springer-Verlag Telos, 1998), pp. 541-546.

A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis (Wiley Interscience, 2001).

[CrossRef]

M. R. Banham and A. K. Katsaggelos, "Digital image restoration," IEEE Signal Process. Mag. 14, 24-41 (1997).

[CrossRef]

P. Kisilev, M. Zibulevsky, and Y. Y. Zeevi, "Multiscale framework for blind separation of linearly mixed signals," J. Mach. Learn. Res. 4, 1339-1363 (2003).

M. Zibulevsky, P. Kisilev, Y. Zeevi, and B. Pearlmutter, "Blind source separation via multinode sparse representation," in Advances in Neural Information Processing Systems (Morgan Kaufman, 2002), pp. 185-191.

I. Kopriva, D. J. Garrood, and V. Borjanovic, "Single frame blind image deconvolution by non-negative sparse matrix factorization," Opt. Commun. 266, 456-464 (2006).

[CrossRef]

I. Kopriva and D. Nuzillard, "Non-negative matrix factorization approach to blind image deconvolution," Lect. Notes Comput. Sci. 3889, 966-973 (2006).

[CrossRef]

I. Kopriva, "Single-frame multichannel blind deconvolution by nonnegative matrix factorization with sparseness constraints," Opt. Lett. 30, 3135-3137 (2005).

[CrossRef]
[PubMed]

I. Kopriva, Q. Du, H. Szu, and W. Wasylkiwskyj, "Independent component analysis approach to image sharpening in the presence of atmospheric turbulence," Opt. Commun. 233, 7-14 (2004).

[CrossRef]

D. Kundur and D. Hatzinakos, "Blind image deconvolution," IEEE Signal Process. Mag. 13, 43-64 (1996).

[CrossRef]

R. L. Lagendijk and J. Biemond, Iterative Identification and Restoration of Images (Kluwer Academic, 1991).

[CrossRef]

W. M. Lam and J. M. Shapiro, "A class of fast algorithms for the Peano-Hillbert space filling curve," in Proceedings of the IEEE International Conference Image Processing (ICIP-94) (IEEE, 1994), Vol. 1, pp. 638-641.

[CrossRef]

D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature 401, 788-791 (1999).

[CrossRef]
[PubMed]

Y. Li, S. Amari, A. Cichocki, D. W. C. Ho, and S. Xie, "Underdetermined blind source separation based on sparse representation," IEEE Trans. Signal Process. 54, 423-437 (2006).

[CrossRef]

Y. Li, A. Cichocki, and S. Amari, "Analysis of sparse representation and blind source separation," Neural Comput. 16, 1193-1234 (2004).

[CrossRef]
[PubMed]

L. B. Lucy, "An iterative technique for rectification of observed distribution," Astron. J. 79, 745-754 (1974).

[CrossRef]

P. McCullagh, Tensor Methods in Statistics (Chapman & Hall, 1995).

M. Numata and N. Hamada, "Image restoration of multichannel blurred images by independent component analysis," in Proceedings of 2004 RISP International Workshop on Nonlinear Circuit and Signal Processing, Hawaii, March 5-7, 2004, pp. 197-200.

I. Kopriva and D. Nuzillard, "Non-negative matrix factorization approach to blind image deconvolution," Lect. Notes Comput. Sci. 3889, 966-973 (2006).

[CrossRef]

A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis (Wiley Interscience, 2001).

[CrossRef]

S. J. Orfanidis, Optimum Signal Processing: An Introduction, 2nd ed. (Macmillan, 1988).

M. Zibulevsky, P. Kisilev, Y. Zeevi, and B. Pearlmutter, "Blind source separation via multinode sparse representation," in Advances in Neural Information Processing Systems (Morgan Kaufman, 2002), pp. 185-191.

M. B. Priestley, Spectral Analysis and Time Series (Academic, 1981).

D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature 401, 788-791 (1999).

[CrossRef]
[PubMed]

W. M. Lam and J. M. Shapiro, "A class of fast algorithms for the Peano-Hillbert space filling curve," in Proceedings of the IEEE International Conference Image Processing (ICIP-94) (IEEE, 1994), Vol. 1, pp. 638-641.

[CrossRef]

J. F. Cardoso and A. Souloumiac, "Blind beamforming for non-Gaussian signals," IEE Proc. F, Radar Signal Process. 140, 362-370 (1993).

[CrossRef]

I. Kopriva, Q. Du, H. Szu, and W. Wasylkiwskyj, "Independent component analysis approach to image sharpening in the presence of atmospheric turbulence," Opt. Commun. 233, 7-14 (2004).

[CrossRef]

T. Tanaka and A. Cichocki, "Subband decomposition independent component analysis and new performance criteria," in Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing (IEEE, 2004), pp. 541-544.

P. Georgiev, F. Theis, and A. Cichocki, "Sparse component analysis and blind source separation of underdetermined mixtures," IEEE Trans. Neural Netw. 16, 992-996 (2005).

[CrossRef]
[PubMed]

S. Umeyama, "Blind deconvolution of blurred images by use of ICA," Electron. Commun. Jpn. Part III: Fund. Electron. Sci. 84, 1-9 (2001).

I. Kopriva, Q. Du, H. Szu, and W. Wasylkiwskyj, "Independent component analysis approach to image sharpening in the presence of atmospheric turbulence," Opt. Commun. 233, 7-14 (2004).

[CrossRef]

M. V. Wickerhauser, Adapted Wavelet Analysis from Theory to Software (Peters, 1994).

Y. Li, S. Amari, A. Cichocki, D. W. C. Ho, and S. Xie, "Underdetermined blind source separation based on sparse representation," IEEE Trans. Signal Process. 54, 423-437 (2006).

[CrossRef]

A. M. Yaglom, Introduction to the Theory of Stationary Random Functions (Prentice Hall, 1962).

A. Cichocki, R. Zdunek, and S. Amari, "Csiszár's divergences for non-negative matrix factorization: family of new algorithms," Lect. Notes Comput. Sci. 3889, 32-39 (2006).

[CrossRef]

M. Zibulevsky, P. Kisilev, Y. Zeevi, and B. Pearlmutter, "Blind source separation via multinode sparse representation," in Advances in Neural Information Processing Systems (Morgan Kaufman, 2002), pp. 185-191.

M. M. Bronstein, A. Bronstein, M. Zibulevsky, and Y. Y. Zeevi, "Blind deconvolution of images using optimal sparse representations," IEEE Trans. Image Process. 14, 726-736 (2005).

[CrossRef]
[PubMed]

P. Kisilev, M. Zibulevsky, and Y. Y. Zeevi, "Multiscale framework for blind separation of linearly mixed signals," J. Mach. Learn. Res. 4, 1339-1363 (2003).

K. Zhang and L. W. Chan, "An adaptive method for subband decomposition ICA," Neural Comput. 18, 191-223 (2006).

[CrossRef]

K. Zhang and L. W. Chan, "Enhancement of source independence for blind source separation," Lect. Notes Comput. Sci. 3889, 731-738 (2006).

[CrossRef]

M. M. Bronstein, A. Bronstein, M. Zibulevsky, and Y. Y. Zeevi, "Blind deconvolution of images using optimal sparse representations," IEEE Trans. Image Process. 14, 726-736 (2005).

[CrossRef]
[PubMed]

P. Kisilev, M. Zibulevsky, and Y. Y. Zeevi, "Multiscale framework for blind separation of linearly mixed signals," J. Mach. Learn. Res. 4, 1339-1363 (2003).

M. Zibulevsky, P. Kisilev, Y. Zeevi, and B. Pearlmutter, "Blind source separation via multinode sparse representation," in Advances in Neural Information Processing Systems (Morgan Kaufman, 2002), pp. 185-191.

L. B. Lucy, "An iterative technique for rectification of observed distribution," Astron. J. 79, 745-754 (1974).

[CrossRef]

S. Umeyama, "Blind deconvolution of blurred images by use of ICA," Electron. Commun. Jpn. Part III: Fund. Electron. Sci. 84, 1-9 (2001).

J. F. Cardoso and A. Souloumiac, "Blind beamforming for non-Gaussian signals," IEE Proc. F, Radar Signal Process. 140, 362-370 (1993).

[CrossRef]

M. R. Banham and A. K. Katsaggelos, "Digital image restoration," IEEE Signal Process. Mag. 14, 24-41 (1997).

[CrossRef]

D. Kundur and D. Hatzinakos, "Blind image deconvolution," IEEE Signal Process. Mag. 13, 43-64 (1996).

[CrossRef]

J. G. Daugman, "Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression," IEEE Trans. Acoust., Speech, Signal Process. 36, 1169-1179 (1988).

[CrossRef]

M. M. Bronstein, A. Bronstein, M. Zibulevsky, and Y. Y. Zeevi, "Blind deconvolution of images using optimal sparse representations," IEEE Trans. Image Process. 14, 726-736 (2005).

[CrossRef]
[PubMed]

C. L. Bryne, "Accelerating the EMML algorithm and related iterative algorithms by rescaled block-iterative methods," IEEE Trans. Image Process. 7, 100-109 (1998).

[CrossRef]

P. Georgiev, F. Theis, and A. Cichocki, "Sparse component analysis and blind source separation of underdetermined mixtures," IEEE Trans. Neural Netw. 16, 992-996 (2005).

[CrossRef]
[PubMed]

Y. Li, S. Amari, A. Cichocki, D. W. C. Ho, and S. Xie, "Underdetermined blind source separation based on sparse representation," IEEE Trans. Signal Process. 54, 423-437 (2006).

[CrossRef]

A. Cichocki and P. Georgiev, "Blind source separationalgorithms with matrix constraints," IEICE Trans. Fundamentals E86-A, 522-531 (2003).

P. Kisilev, M. Zibulevsky, and Y. Y. Zeevi, "Multiscale framework for blind separation of linearly mixed signals," J. Mach. Learn. Res. 4, 1339-1363 (2003).

J. F. Cardoso, "Dependence, correlation and Gaussianity in independent component analysis," J. Mach. Learn. Res. 4, 1177-1203 (2003).

P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints," J. Mach. Learn. Res. 5, 1457-1469 (2004).

I. Kopriva and D. Nuzillard, "Non-negative matrix factorization approach to blind image deconvolution," Lect. Notes Comput. Sci. 3889, 966-973 (2006).

[CrossRef]

A. Cichocki, R. Zdunek, and S. Amari, "Csiszár's divergences for non-negative matrix factorization: family of new algorithms," Lect. Notes Comput. Sci. 3889, 32-39 (2006).

[CrossRef]

K. Zhang and L. W. Chan, "Enhancement of source independence for blind source separation," Lect. Notes Comput. Sci. 3889, 731-738 (2006).

[CrossRef]

D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature 401, 788-791 (1999).

[CrossRef]
[PubMed]

K. Zhang and L. W. Chan, "An adaptive method for subband decomposition ICA," Neural Comput. 18, 191-223 (2006).

[CrossRef]

Y. Li, A. Cichocki, and S. Amari, "Analysis of sparse representation and blind source separation," Neural Comput. 16, 1193-1234 (2004).

[CrossRef]
[PubMed]

I. Kopriva, D. J. Garrood, and V. Borjanovic, "Single frame blind image deconvolution by non-negative sparse matrix factorization," Opt. Commun. 266, 456-464 (2006).

[CrossRef]

I. Kopriva, Q. Du, H. Szu, and W. Wasylkiwskyj, "Independent component analysis approach to image sharpening in the presence of atmospheric turbulence," Opt. Commun. 233, 7-14 (2004).

[CrossRef]

P. Comon, "Independent component analysis, a new concept?" Signal Process. 36, 287-314 (1994).

[CrossRef]

D. R. Brillinger, Time Series Data Analysis and Theory (McGraw-Hill, 1981).

P. McCullagh, Tensor Methods in Statistics (Chapman & Hall, 1995).

M. V. Wickerhauser, Adapted Wavelet Analysis from Theory to Software (Peters, 1994).

R. L. Lagendijk and J. Biemond, Iterative Identification and Restoration of Images (Kluwer Academic, 1991).

[CrossRef]

M. B. Priestley, Spectral Analysis and Time Series (Academic, 1981).

W. M. Lam and J. M. Shapiro, "A class of fast algorithms for the Peano-Hillbert space filling curve," in Proceedings of the IEEE International Conference Image Processing (ICIP-94) (IEEE, 1994), Vol. 1, pp. 638-641.

[CrossRef]

A. M. Yaglom, Introduction to the Theory of Stationary Random Functions (Prentice Hall, 1962).

A. Hyvärinen, "Independent component analysis for time-dependent stochastic processes," in Proceedings of the International Conference on Artificial Neural Networks (Springer-Verlag Telos, 1998), pp. 541-546.

S. J. Orfanidis, Optimum Signal Processing: An Introduction, 2nd ed. (Macmillan, 1988).

M. Zibulevsky, P. Kisilev, Y. Zeevi, and B. Pearlmutter, "Blind source separation via multinode sparse representation," in Advances in Neural Information Processing Systems (Morgan Kaufman, 2002), pp. 185-191.

M. Numata and N. Hamada, "Image restoration of multichannel blurred images by independent component analysis," in Proceedings of 2004 RISP International Workshop on Nonlinear Circuit and Signal Processing, Hawaii, March 5-7, 2004, pp. 197-200.

A. Cichocki, "Blind source separation: new tools for extraction of source signals and denoising," in Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, H.Szu, ed., Proc. SPIE 5818, 11-25 (2005).

T. Tanaka and A. Cichocki, "Subband decomposition independent component analysis and new performance criteria," in Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing (IEEE, 2004), pp. 541-544.

A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis (Wiley Interscience, 2001).

[CrossRef]

A. Cichocki and S. Amari, Adaptive Blind Signal and Image Processing (Wiley, 2002).

[CrossRef]