\input zb-basic
\input zb-ioport
\iteman{io-port 06033823}
\itemau{Ashino, Ryuichi; Kataoka, Shusuke; Mandai, Takeshi; Morimoto, Akira}
\itemti{Blind image source separations by wavelet analysis.}
\itemso{Appl. Anal. 91, No. 4, 617-644 (2012).}
\itemab
Summary: The purpose of blind source separation is to separate and to estimate the original sources from the sensor array, without knowing the transmission channel characteristics. Besides methods based on independent component analysis which is one of the most powerful tools for blind source separation, several methods based on time-frequency analysis have been proposed. One of them is the quotient signal estimation method which can estimate the unknown number of sources. The notion of the continuous multiwavelet transform is introduced and three types of multiwavelets are presented. A new method using continuous multiwavelet transform, position-scale information matrices and self-organizing maps, is presented and applied to image source separations with noise. The performances of three multiwavelets are compared.
\itemrv{~}
\itemcc{}
\itemut{blind image source separation; continuous multiwavelet transform; time-frequency analysis}
\itemli{doi:10.1080/00036811.2011.616497}
\end