Denoising using adaptive thresholding and higher order statistics

Samuel P. Kozaitis; Tim Young

doi:10.1117/12.818719

19 March 2009 Denoising using adaptive thresholding and higher order statistics

Samuel P. Kozaitis, Tim Young

Proceedings Volume 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII; 73430F (2009) https://doi.org/10.1117/12.818719
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Samuel P. Kozaitis and 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); https://doi.org/10.1117/12.818719

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