Proceedings Paper
Denoising using adaptive thresholding and higher order statistics| Format | Member Price | Non-Member Price |
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Paper Abstract
We showed that a hard threshold for wavelet denoising based on higher order statistics is comparable to a second
order soft threshold. The hard threshold can made adaptive by using a third order statistic as an estimate of the
noise. In addition, the relationship between an adaptive hard threshold and retaining a fraction of wavelet
coefficients is shown. Qualitative and quantitative metrics based on the mean-squared error are used to compare the
hard thresholding and a soft-thresholding technique, BayesShrink.
Paper Details
Date Published: 19 March 2009
PDF: 10 pages
Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73430F (19 March 2009); doi: 10.1117/12.818719
Published in SPIE Proceedings Vol. 7343:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII
Harold H. Szu; F. Jack Agee, Editor(s)
PDF: 10 pages
Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73430F (19 March 2009); doi: 10.1117/12.818719
Show Author Affiliations
Samuel P. Kozaitis, Florida Institute of Technology (United States)
Tim Young, Florida Institute of Technology (United States)
Published in SPIE Proceedings Vol. 7343:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII
Harold H. Szu; F. Jack Agee, Editor(s)
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