Application Of Wavelet Transform In Signal Denoise

For removing the vide varity of different noise that arises in mobile communication,the paper studied the wavelet transform and a new denosing technique in the wavelet domain.Frequency-domain filtering, the classical method,turns out many filter design methods for applications under the assumption of apparent difference between the spectrum of signal and noise. Wiener filtering and Kalman filtering,which developed in 1940's,give optimal de-noising result by applying these conventional methods under their assumptions,but beyond these assumptions,we can not access it.The conventional methods are based on time domain recursive algorithms and the Fourier analysis.Time domain and frequency domain are different viewpoints for a signal.While we can't specify what happened on what frequency,time-frequency analysis is introduced.Wavelet is a new powerful tool of time-frequency analysis is introduced,wavelet is a new ,powerful tool of time-frequency analysis .Due to its quick convergence,irregularity,wavelet is suitde to detect transients.Wavelets are a very interesting class of functions because of their special properties.The orthonormal bases can be constructed by translation and dilation of a mother wavelet,Wavelets have local property in time domain and frequency domain,so we can extract information from signals using wavelets.Wavelet analysis theory emerges as a new powerful mathemmatical tool in signal de-noise .This paper applies wavelet theory to process data analysis,mostly focusing on signal de-noise.In this paper I developed a de-noise method based on translation invariance wavelet transform, The method performed the cycle-spinning for the signal to be de-noised. And then, the soft/hard threshold was used to shrink the wavelet coefficient of the signal and reconstruct the signal, The metohd can suppress pseudo-Gibbs phenomena on the singularity point of signal produced by de-noise algorithm based on wavelet shrinkage.On comprehensive de-noising tests of SNR and peak pick and smoothness,wavelet wins, over the conventional methods. However, wavelet method presents resemblaces in forms or principles with conventional ones. In practice, there are many challenges to wavelet de noising. We propose some problems about de-noising and give solutions.