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Astronomy: NOISE REMOVAL USING PRINCIPAL COMPONANT ANALYSIS

 

NOISE REMOVAL USING PRINCIPAL COMPONANT ANALYSIS

    Nihad Ali Karam       

Department of Astronomy, College of Science, University of Baghdad

-Jadiryah, Baghdad, Iraq Received: 10/3/2001 Accepted:7/3/2002

ABSTRACT

An algorithm for Hotelling transformation has been presented and applied for smoothing and Enhancing a set of images Corrupted by linear additive with different values of signal to noise ratio.

Hotelling transformation is used to transform this set of images in to a set of characteristic value and scalar multiplier.

This transformation minimizes the mean sequare error when only a finite number of basic vector are adopted





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