19 March 2009 Denoising using adaptive thresholding and higher order statistics
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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.
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Samuel P. Kozaitis, Samuel P. Kozaitis, Tim Young, Tim Young, } "Denoising using adaptive thresholding and higher order statistics", Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73430F (19 March 2009); doi: 10.1117/12.818719; https://doi.org/10.1117/12.818719
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