A B-wavelet-based noise-reduction algorithm

A wavelet-based method is introduced for removing structured noise (e.g., impulsive spikes or unwanted harmonic components) from data. For this type of noise, the time- and frequency-localization capabilities of wavelets provide better noise detection and less signal distortion than direct filtering of data. The procedure is applied to time-series data with impulsive noise and transfer-function data with multipath interference. The authors use a single set of scaling and wavelet bases that can effectively represent a wide range of signals, and then they perform noise reducing operations on the scaling and wavelet coefficients. Multiresolution analysis is introduced in order to describe the noise reduction algorithm