DocumentCode
1856074
Title
Threshold selection for wavelet shrinkage of noisy data
Author
Donoho, David L. ; Johnstone, Iain M.
Author_Institution
Dept. of Stat., Stanford Univ., CA, USA
fYear
1994
fDate
3-6 Nov 1994
Abstract
Methods based on thresholding and shrinking empirical wavelet coefficients hold promise for recovering and/or denoising signals observed in noise. Here the authors review and compare various proposals for the choice of thresholds. These include soft and hard thresholding, and thresholds that are fixed in advance or chosen level by level from an empirical optimality criterion. The authors present results from simulations and real data examples
Keywords
noise; signal processing; wavelet transforms; empirical optimality criterion; hard thresholding; noisy data; real data; signal denoising; signal recovery; simulated data; soft thresholding; threshold selection; wavelet shrinkage; Costs; Gaussian noise; Inverse problems; Noise level; Noise reduction; Proposals; Statistics; Wavelet coefficients; Wavelet transforms; White noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE
Conference_Location
Baltimore, MD
Print_ISBN
0-7803-2050-6
Type
conf
DOI
10.1109/IEMBS.1994.412133
Filename
412133
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