The Research Of Signal And Image Denoising Based On Wavelet Theory

Wavelet has good localizing quality at time domain and frequencydomain simultaneously and the characteristic of multi-resolution ratioanalysis, so it can fulfill all kinds of wave-filtering needs such aslow-pass, high-pass, sink wave, random noise denoising. Compare withtraditional wave-filtering methods, wavelet has incomparable advantage.Wavelet has become an effective means of signal analysis. Especially theWavelet Shrinkage is widely used in noise denosing for its simplecomputation. But the detail of signal or image will be lost in this methodand the thresholding function in the algorithm has many disadvantages.On the other side, because the mother wavelets is a gather, so whichmother wavelet can get best denoising effect at practical application is anawait-resolving research subject which has practical value.First, the application of wavelet in signal denoising is studied. Afterthe analysis and study of the classical wavelet shrinkage denoisingmethod, a new thresholding function based on the wavelet shrinkage ispresented, as its modified project, in order to improve the denoising effect.This modified project can overcome the shortcomings of the classicalthresholding denoising methods in some extent, moreover, it can be applied to the adaptive wavelet thresholding denoising algorithm, whichis verified through a good many of simulation experiments. Second, theapplication of wavelet in image denoising is studied. Two new imagede-noising approaches based on combination of traditional methods andwavelet shrinkage are put forward. In these new approaches, the differentnoises in images are filtered out while the edges of images are wellpreserved. Simulation result shows that the new approaches presented inthis paper are more effective and predominant than other image denoisingmethods. Finally, how to choose the mother wavelet is studied and a nearstandard is found in the paper.