The Study On Wavelet Analysis And Its Application To Signal De-noising

Signal and information processing is one of the most rapidly developed subject in the field of information science in recent twenty years. The signal processing mainly included signal de-noising, character abstracting and border abstracting. Signal de-noising is to be the most common in the signal processing, the classical signal de-noising method such as the pure time domain method, the pure frequency region method, Fourier transform method, the window Fourier transform method. But these methods all have limitation in the real application. The wavelet transform is a new development unite time domain with frequency region analysis method in 1980's. And it all has well localization characteristic property in time domain and frequency region. The wavelet transform has got broad application in signal de-noising.The fundamental theories of wavelet analysis are discussed in detail. Continuous wavelet transform, discrete wavelet transform and dyadic wavelet transform are introduced. 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.The principles of wavelet transform modulus maxima de-noising method are introduced in detail, an analysis of the choice of some parameters in the process of de-noising is made in detail, and some choice grounds are given. Some key problems on de-noising method based on wavelet threshold are discussed in detail, and some improvement schemes are proposed, and the simulation testing has proved the effectiveness of the schemes. A combination de-noising algorithm based on the spatial correlation-based algorithm is presented, the experimental results show that the filtered wavelet coefficients in the proposed algorithm have the advantages of good continuity, high accuracy and convenience for signal reconstruction.