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Blind Source Separation Using Hessian Evaluation
Jyothirmayi M, Elavaar Kuzhali S, Sethu Selvi S
Pages - 207 - 218     |    Revised - 15-07-2012     |    Published - 10-08-2012
Volume - 6   Issue - 4    |    Publication Date - August 2012  Table of Contents
Blind Source Separation, Sparseness, Hessian Evaluation
This paper focuses on the blind image separation using sparse representation for natural images. The statistics of the natural image is based on one particular statistical property called sparseness, which is closely related to the super-gaussian distribution. Since natural images can have both gaussian and non gaussian distribution, the original infomax algorithm cannot be directly used for source separation as it is better suited to estimate the super-gaussian sources. Hence, we explore the property of sparseness for image representation and show that it can be effectively used for blind source separation. The efficiency of the proposed method is compared with other sparse representation methods through Hessian evaluation.
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Associate Professor Jyothirmayi M
MSRIT - India
Mr. Elavaar Kuzhali S
MSRIT - India
Professor Sethu Selvi S
MSRIT - India