Blind Extraction of Sparse Images from under-determined Mixtures

We propose a blind signal extraction approach to the extraction of binary and sparse images from their under-determined mixtures, i.e. when the number of sensors is lower by one than the number of unknown sources. A practically fea- sible solution is proposed for constrained classes of images, i.e. sparse, binary- valued and dynamically-constrained sources.

[1]  E. Oja,et al.  Independent Component Analysis , 2001 .

[2]  Ruey-Wen Liu,et al.  General approach to blind source separation , 1996, IEEE Trans. Signal Process..

[3]  David L. Donoho,et al.  Application of basis pursuit in spectrum estimation , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[4]  Bhaskar D. Rao,et al.  Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..

[5]  Juha Karhunen,et al.  On Neural Blind Separation with Noise Suppression and Redundancy Reduction , 1997, Int. J. Neural Syst..

[6]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[7]  Yuanqing Li,et al.  Analysis of Sparse Representation and Blind Source Separation , 2004, Neural Computation.

[8]  Rajesh P. N. Rao Bayesian Computation in Recurrent Neural Circuits , 2004, Neural Computation.

[9]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .