ABSTRACT
Wavelet analysis has been developed as multiresolution signal processing, which is used effectively for signal and image processing, compression, computer vision, medical imaging, etc. [81]-[85]. In wavelet analysis, a fully scalable modulated window is used for frequency localization [88]-[91]. The window is sliding, and the wavelet transform of a part of the signal is calculated for every position. The result of the wavelet transform is a collection of time-scaling representations of the continuous-time signal with different resolutions. In other words, wavelet methods are referred to as methods of cross correlations of the signal with a given family of scaled waves. In contrast, the Fourier transform is considered as a transform without time resolution, since the basis cosine and sine functions are defined everywhere on the real line. Each Fourier component depends on the global behavior of the signal.
