Conclusion

Wavelets were introduced in the early 1980s, in the context of signal analysis and oil exploration. At the time, the aim was to represent signals in such a way as to simultaneously display temporal information (location in time and therefore in space, duration) and frequency information, thus facilitating the identification of the physical characteristics of the subsurface.

Because of their correlation capability (separation of noise and useful signal) and the notion of parsimony in their representation, one of the key applications of orthogonal wavelet transforms is filtering, more commonly known as denoising.

Indeed, in denoising, wavelet-based multiresolution analysis has made a major contribution to a clear evolution in the acquisition, measurement and processing of signals and images using linear methods and mainly non-linear adaptive...