By Topic

Signal waveform restoration by wavelet denoising

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

2 Author(s)
Wu, S.Q. ; Dept. of Electron. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong ; Ching, P.C.

In this paper, we first establish an approximated sampling theorem for an arbitrary continuous signal which is essential for wavelet analysis. The differences and similarities between quadrature mirror filter decomposition and wavelet decomposition are contrasted. We then propose an efficient way to recover a source signal buried in white noise by using wavelet denoising. The method is capable of reducing the mean square error bound from O(log2(n)) to O(log(n)), where n is the number of samples. It is also shown that the new estimator is asymptotically unbiased if the source signal is a piece-wise polynomial

Published in:

Time-Frequency and Time-Scale Analysis, 1996., Proceedings of the IEEE-SP International Symposium on

Date of Conference:

18-21 Jun 1996