Cart (Loading....) | Create Account
Close category search window
 

De-noising by soft-thresholding

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Donoho, D.L. ; Dept. of Stat., Stanford Univ., CA, USA

Donoho and Johnstone (1994) proposed a method for reconstructing an unknown function f on [0,1] from noisy data di=f(ti )+σzi, i=0, …, n-1,ti=i/n, where the zi are independent and identically distributed standard Gaussian random variables. The reconstruction fˆ*n is defined in the wavelet domain by translating all the empirical wavelet coefficients of d toward 0 by an amount σ·√(2log (n)/n). The authors prove two results about this type of estimator. [Smooth]: with high probability fˆ*n is at least as smooth as f, in any of a wide variety of smoothness measures. [Adapt]: the estimator comes nearly as close in mean square to f as any measurable estimator can come, uniformly over balls in each of two broad scales of smoothness classes. These two properties are unprecedented in several ways. The present proof of these results develops new facts about abstract statistical inference and its connection with an optimal recovery model

Published in:

Information Theory, IEEE Transactions on  (Volume:41 ,  Issue: 3 )

Date of Publication:

May 1995

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.