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Proceedings Paper

Singular Value Decomposition And Digital Image Processing
Author(s): Methodi Kovatchev; Eugene Mitev; Rumiana Nedkova
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Paper Abstract

Two methods for decreasing variation due to additive noise into an image are discussed. Both methods are based on Singular Values Decomposition (SVD) of given Image matrix: • The singular values take the meaning of the dispersion coefficients, and the Image reconstruction by part of the basis functions leads to entropy minimization of the image, guaranteeing minimization of the least-squares error. The sharing criterion is used by the first method to extract the most significant coefficients. • Another discussed method Is a filter fitting to the singular value spectrum of a noisy matrix. In the case of known noise distribution the filter is noise matched.

Paper Details

Date Published: 12 April 1990
PDF: 4 pages
Proc. SPIE 1183, Holography '89, (12 April 1990); doi: 10.1117/12.963865
Show Author Affiliations
Methodi Kovatchev, Bulgarian Academy of Sciences (Bulgaria)
Eugene Mitev, Bulgarian Academy of Sciences (Bulgaria)
Rumiana Nedkova, Bulgarian Academy of Sciences (Bulgaria)

Published in SPIE Proceedings Vol. 1183:
Holography '89
Yuri N. Denisyuk; Tung H. Jeong, Editor(s)

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