Image De-noising Method Based On Wavelet Transform

Wavelet shrinkage is a method that creating certain regulation according to thedifferent representation of signal and noise in wavelet domain and processing thewavelet coefficients. The essential lies in shrinking or deleting the coefficients raisedfrom noise and reserve those raised from signal.There are manyde-noising methods based on wavelet transform, in this paper,which were discussed at threshold de-noising. An analysis of the choice of threshold,threshold function and some parameters in the process of de-noising is made in detail,and some choice grounds are given. Some keyproblems on de-noising method based onwavelet threshold are discussed in detail,and some improvement schemes are proposed,and the simulation testing has proved the effectiveness of the schemes.In order to overcome the defects of the traditional threshold de-noising, such as donot take a priori information of wavelet coefficients into account, An image de-noisingmethod of wavelet shrinkage threshold based on BayesShrink is discussed in detail. Andan image de-noising method of improved BayesShrink threshold combined Wienerfilter is presented. Because the Wiener filter is a best linear filter, so combining waveletthresholding de-noising with the Wiener filter, and present an image de-noising methodof WienerShrink threshold. Last but not least important, wavelet based image de-nosinguse adaptive thresholding is presented. The experimental results show that both themethods above-presented are practical, efficient and superiority.