Blind source separation of images based on general cross correlation of linear operators

Noam Shamir; Zeev Zalevsky; Leonid P. Yaroslavsky; Bahram Javidi

doi:10.1117/1.3596620

1 April 2011 Blind source separation of images based on general cross correlation of linear operators

Noam Shamir, Zeev Zalevsky, Leonid P. Yaroslavsky, Bahram Javidi

Author Affiliations +

Journal of Electronic Imaging, Vol. 20, Issue 2, 023017 (April 2011). https://doi.org/10.1117/1.3596620

Abstract

Blind source separation is a process in which mixed signals, obtained as a linear combination of various source signals, are decomposed into their original sources. The source signals and their mixture weights are unknown, but a priori information about their statistical behavior and mixing model is available. In this paper, a new algorithm based on generalized cross correlation linear-operator set is proposed. This algorithm significantly improves source-separation quality compared to several other well-known algorithms, such as subband decomposition independent component analysis, block Gaussian likelihood, and convex analysis of mixtures of non-negative sources.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Noam Shamir, Zeev Zalevsky, Leonid P. Yaroslavsky, and Bahram Javidi "Blind source separation of images based on general cross correlation of linear operators," Journal of Electronic Imaging 20(2), 023017 (1 April 2011). https://doi.org/10.1117/1.3596620

Published: 1 April 2011

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