3 April 2008 Thresholding for higher-order statistical denoising
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Abstract
Hard thresholding seems to work well for denoising signals using higher-order statistics. We statistically examined the best values for hard thresholding and related this to the fraction of wavelet coefficients set to zero to obtain the minimum MSE. In addition, we found that the minimum MSE obtained was less sensitive to the threshold when implemented based on a third-order parameter rather than the noise power. Alternatively, we found that this approach to thresholding could be implemented by setting a fixed fraction of wavelet coefficients to zero.
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Samuel P. Kozaitis, Samuel P. Kozaitis, Tim Young, Tim Young, } "Thresholding for higher-order statistical denoising", Proc. SPIE 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI, 69790O (3 April 2008); doi: 10.1117/12.777155; https://doi.org/10.1117/12.777155
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