Information-to-noise improvement in the frequency domain using the wavelet transform

Charles R. Thompson; Brian DeFacio

doi:10.1117/12.139008

29 December 1992 Information-to-noise improvement in the frequency domain using the wavelet transform

Charles R. Thompson, Brian DeFacio

Author Affiliations +

Proceedings Volume 1767, Inverse Problems in Scattering and Imaging; (1992) https://doi.org/10.1117/12.139008
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

Some finite energy, causal, real-valued signals are frequency analyzed using a fast Fourier transform (FFT). Computer generated additive white noise is combined with time-domain samples, and the analysis is repeated. It is shown that the combination of noise and aliasing can hide information when high frequency noise is folded over an interesting part of the frequency domain. Aliasing cannot be avoided in exploratory physics and engineering experiments and aliased noise can drown out real peaks in the signal frequency spectrum. Time-domain filtering techniques are discussed, but they cannot always recover these lost peaks. A new method is introduced in which the wavelet transform and its inverse are used to suppress high frequency noise prior to Fourier analysis. This method is useful in improving on experimental results when aliasing of high frequency noise presents a problem.

Citation Download Citation

Charles R. Thompson and Brian DeFacio "Information-to-noise improvement in the frequency domain using the wavelet transform", Proc. SPIE 1767, Inverse Problems in Scattering and Imaging, (29 December 1992); https://doi.org/10.1117/12.139008

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE