Hostname: page-component-76c49bb84f-887v8 Total loading time: 0 Render date: 2025-07-06T23:54:31.733Z Has data issue: false hasContentIssue false
  • English
  • Français

Classical low-pass filter and real-time wavelet-based denoising technique implementedon a DSP: a comparison study*

Published online by Cambridge University Press:  25 October 2002

Ch. Dolabdjian ,
J. Fadili  and
E. Huertas Leyva
Ch. Dolabdjian*
Affiliation:
Groupe de Recherche en Informatique, Image et Instrumentation de Caen (CNRS UMR 6072), ISMRA et Université de Caen, 6 Bd du Maréchal Juin, 14050 Caen Cedex, France
J. Fadili
Affiliation:
Groupe de Recherche en Informatique, Image et Instrumentation de Caen (CNRS UMR 6072), ISMRA et Université de Caen, 6 Bd du Maréchal Juin, 14050 Caen Cedex, France
E. Huertas Leyva
Affiliation:
Groupe de Recherche en Informatique, Image et Instrumentation de Caen (CNRS UMR 6072), ISMRA et Université de Caen, 6 Bd du Maréchal Juin, 14050 Caen Cedex, France
*
C.Dolabdjian@greyc.ismra.fr
Get access

Abstract

We have implemented a real-time numerical denoising algorithm, using the Discrete Wavelet Transform (DWT), on a TMS320C3x Digital Signal Processor (DSP). We also compared from a theoretical and practical viewpoints this post-processing approach to a more classical low-pass filter. This comparison was carried out using an ECG-type signal (ElectroCardiogram). The denoising approach is an elegant and extremely fast alternative to the classical linear filters class. It is particularly adapted to non-stationary signals such as those encountered in biological applications. The denoising allows to substantially improve detection of such signals over Fourier-based techniques. This processing step is a vital element in our acquisition chain using high sensitivity magnetic sensors. It should enhance detection of cardiac-type magnetic signals or magnetic particles in movement.

Keywords

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

*

This paper has been first presented orally at the C2I colloquium in February 2001

References

Dolabdjian, C., Saez, S., Rayes Toledo, A., Colloque Interdiscplinaire en Instrumentation C2I'98 1, 683 (1998).
A. Aldroubi, M. Unser, Wavelets in Medicine and Biology (CRC Press, Boca Raton FL USA, 1996).
M.V. Wickerhauser, Adapted wavelet analysis: from theory to software (Wellesley IEEE Press, New York, 1994).
S.G. Mallat, A Wavelet tour of signal processing second edition (Academic Press, New York, 1999).
I. Daubechies, Ten lectures on wavelets (PA SIAM, Philadelphia, 1992).
Donoho, D.L., Johnstone, I.M., Biometrika 81, 425 (1999). CrossRef
D.L. Donoho, I.M. Johnstone, Minimax estimation via Wavelet Shrinkage, Annals of statistics (1994).
A.G. Bruce, H.-Y. Gao, Understanding WaveShrink: Variance and Bias Estimation, Technical report StatSci Division MathSoft Inc, 1995.
R. Coifman, D.L. Donoho, Translation-invariant denoising, Technical Report 475, Dept of Statistics Stanford University 1999.
Mallat, S.G., IEEE Trans. PAMI 11, 674 (1989). CrossRef