Signal waveform restoration by wavelet denoising

Author

Wu, S.Q. ; Ching, P.C.

Author_Institution

Dept. of Electron. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong

fYear

1996

fDate

18-21 Jun 1996

Firstpage

89

Lastpage

92

Abstract

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

Keywords

piecewise polynomial techniques; signal restoration; signal sampling; waveform analysis; wavelet transforms; white noise; approximated sampling theorem; arbitrary continuous signal; mean square error bound; piece-wise polynomial; quadrature mirror filter decomposition; signal waveform restoration; source signal; wavelet analysis; wavelet decomposition; wavelet denoising; white noise; Continuous wavelet transforms; Filters; Mean square error methods; Mirrors; Noise reduction; Sampling methods; Signal analysis; Signal restoration; Wavelet analysis; White noise;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Paris

Print_ISBN

0-7803-3512-0

Type

conf

DOI

10.1109/TFSA.1996.546693

Filename

546693