Blind source separation of images based upon fractional autocorrelation

Noam Shamir, Natan Kopeika, Zeev Zalevsky

Research output: Contribution to journalArticlepeer-review

Abstract

Blind source separation (BSS) is a process in which mixed signals are separated into their original sources. Both the sources as well as the mixing coefficients are unknown but a priori information about statistical behavior and about the mixing model might be available. We here suggest a generalization of our previous research that showed a new BSS algorithm based on general cross correlation linear operators applied on the sources that are to be separated. In that approach in cases of negligible cross-correlation between the source signals, a very good separation could be obtained. Here we propose to use the fractional Fourier transform in order to reduce the correlation between the source signals and to further enhance the obtained separation performance. We present reduced dependence on the cross-correlation between the source images, resulting in better separation of the mixed sources.

Original languageEnglish
Article number12041
JournalJournal of Electronic Imaging
Volume21
Issue number4
DOIs
StatePublished - 1 Dec 2012

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