Abstract
The recovery of process information from noisy data (de-noising) is studied by investigating the classical solution of the estimation problem first. Next, the effectiveness of wavelet-based algorithms for data recovery is considered. A novel method based on coefficient de-noising according to WienerShrink method of wavelet thresholding is proposed. Simulation results are presented, highlighting the advantages of the de-noising method over the classical approaches based on the mean square error criterion.
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