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dc.contributor.authorKozaitis, Samuel P.
dc.contributor.authorYoung, Tim
dc.date.accessioned2017-06-13T14:32:15Z
dc.date.available2017-06-13T14:32:15Z
dc.date.issued2009-03-19
dc.identifier.citationKozaitis, S. P., & Young, T. (2009). Denoising using adaptive thresholding and higher order statistics. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 7343 doi:10.1117/12.818719en_US
dc.identifier.urihttp://hdl.handle.net/11141/1538
dc.description.abstractWe 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 be 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.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleDenoising using adaptive thresholding and higher order statisticsen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.818719


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