Simple Denoising Algorithm Using Wavelet Transform
Abstract
We have presented a new and alternative algorithm for noise reduction using the methods of discrete wavelet transform and numerical differentiation of the data. In our method the threshold for reducing noise comes out automatically. The algorithm has been applied to three model flow systems - Lorenz, Autocatalator, and Rossler systems - all evolving chaotically. The method is seen to work well for a wide range of noise strengths, even as large as 10% of the signal level. We have also applied the method successfully to noisy time series data obtained from the measurement of pressure fluctuations in a fluidized bed, and also to that obtained by conductivity measurement in a liquid surfactant experiment. In all the illustrations we have been able to observe that there is a clean separation in the frequencies covered by the differentiated signal and white noise.
Cite
@article{arxiv.nlin/0002028,
title = {Simple Denoising Algorithm Using Wavelet Transform},
author = {Manojit Roy and V. Ravi Kumar and B. D. Kulkarni and John Sanderson and Martin Rhodes and Michel vander Stappen},
journal= {arXiv preprint arXiv:nlin/0002028},
year = {2007}
}
Comments
15 pages, 5 figures, 1 Table