Signal denoising using the Stationary Wavelet Decomposition

Eleonora Stefanutti, Fabio Bruni

Abstract

We propose a method to denoise 1D experimental signals using wavelet transform. Noise affecting experimental signals is indeed a problem shared by many different scientific and engineering fields and a proper strategy of denoising, avoiding the loss of useful information embedded in the original signal, is often essential. This is especially true for spectroscopic data, where significant features may be hidden by noise or covered by undesired components, which are not related to the physical content of the signal. In particular, we have applied the Discrete Wavelet Transform (DWT) to infrared spectra collected at a synchrotron source, overcoming the limitations of other filtering strategies conventionally employed. The good results here obtained, and the other attempts presented in the recent literature, suggest that wavelet transform can represent a valuable tool, finding its ideal application in a number of many and diverse fields, including spectroscopy, marine science, meteorology, and engineering.

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IMEKO-TC19-METROSEA-2017-21.pdf

DOI

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IMEKO TC

TC19 - Environmental Measurements

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