Denoising using adaptive thresholding and higher order statistics
Samuel P. Kozaitis, Tim Young · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
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.