Georgios Evangelatos, Ioannis Kougioumtzoglou, Isaac Hernandez-fajardo, Xin Ming
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- Wavelets are an alternative tool for signal decomposition using orthogonal functions. Unlike basic Fourier analysis, wavelets do not lose completely time information, a feature that makes the technique suitable for applications where the temporal location of the signal’s frequency content is important. One of the fields where wavelets have been successfully applied is data analysis. In particular, it has been demonstrated that wavelets produce excellent results in signal denoising i.e. the removal of noise from an unknown signal. Shrinkage methods for noise removal, first introduced by Donoho in 1993, have led to a variety of approaches combining wavelets with probabilistic concepts leading to new efficient denoising procedures. This work presents a summary of basic methods for noise removal. Their main features and limitations are discussed and a comparison study is developed. A signal contaminated with Gaussian additive noise is used as testbed for the methods. Conclusions on the performance of the methods, based upon computational efficiency and number of terms used for decomposition, are presented.