Abstract
The wavelet transform, also called wavelet decomposition, recently introduced into the applied sciences and available as software packages, is a powerful method for smoothing experimental data. The wavelet transform is a mathematical transform for hierarchically decomposing functions. It leads to a description of a function, including discrete data vectors or matrices, in terms of a coarse overall shape and details of a graded sequence. This decomposition is the basis for noise reduction. At the various levels of decomposition the coarse coefficients are due to the characteristic signals and part of the details may be interpreted as noise. The method will be discussed on examples of peak recognition in infrared spectroscopy. We will show that some of the wavelet bases lead to a very good compromise between signal/noise ratio enhancement and preservation of the real data structures. Subsequently it enables a Teak ‘Picker’ to find the local maxima of the curve corresponding to real data structures.
Published Version
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