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
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;
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
DOI :
10.1109/IEMBS.1994.412133
