Abstract

We address the problem of Blind Source Separation(BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis(ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to estimate the original intensities in the stated situation. We combine the advantages of existing sparse methods and Kernel ICA in our technique, by proposing wavelet packet based sparse decomposition of signals prior to the application of Kernel ICA. Simulations and experimental results illustrate the effectiveness and accuracy of the proposed approach. The approach is general in the way that it can be tailored and applied to a wide range of BSS problems concerning one-dimensional signals and images(two-dimensional signals).

© 2008 Optical Society of Korea

Blind source separation of chaotic laser signals by independent component analysis

Masahiko Kuraya, Atsushi Uchida, Shigeru Yoshimori, and Ken Umeno
Opt. Express 16(2) 725-730 (2008)

Approach to blind image deconvolution by multiscale subband decomposition and independent component analysis

Ivica Kopriva
J. Opt. Soc. Am. A 24(4) 973-983 (2007)

Implementation of blind source separation for optical fiber sensing

Qiang Li, Zhi Wang, Zejia Huang, Kaili Guo, and Lanlan Liu
Appl. Opt. 53(9) 1832-1837 (2014)

Photonic independent component analysis using an on-chip microring weight bank

Philip Y. Ma, Alexander N. Tait, Thomas Ferreira de Lima, Chaoran Huang, Bhavin J. Shastri, and Paul R. Prucnal
Opt. Express 28(2) 1827-1844 (2020)

Blind source separation with integrated photonics and reduced dimensional statistics

Philip Y. Ma, Alexander N. Tait, Weipeng Zhang, Emir Ali Karahan, Thomas Ferreira de Lima, Chaoran Huang, Bhavin J. Shastri, and Paul R. Prucnal
Opt. Lett. 45(23) 6494-6497 (2020)