Wavelet Denoising And Applying In Signal Processing

Wavelet analysis is a rapidly developing and novel subject. Nowadays,it has been widedly used in practical applications. To study the new theory,methods and applications of wavelet is of great theoretical significance and practical value. This dissertation aims to consummate the wavelet theory,present some new wavelet de-noising algorithms and develop the new scopes of wavelet applications. The thesis mainly includes the following aspects:The fundamental theories of wavelet analysis are discussed in detail. Continuous wavelet transform,discrete wavelet transform and dyadic wavelet transform are included. The fast algorithm of discrete dyadic wavelet transform is given. Finally,an analysis is made on the influence of the wavelet bases on practical applications by studying their mathematical properties.After the analysis and study of the classical thresholding denoising methods,this paper presents a new thresholding function based on them,as their modified project,in order to improve the denoising effect. This modified project can overcome the shortcomings of the classical thresholding denoising methods in some extent. Moreover,it can be applied to the adaptive wavelet thresholding denoising algorithm,which is verified through a good many of simulation experiments.