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

Noam Shamir, Zeev Zalevsky, Leonid Yaroslavsky, Bahram Javidi

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number023017
JournalJournal of Electronic Imaging
Volume20
Issue number2
DOIs
StatePublished - Apr 2011

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