Wavelet Transform In Image Denoising Applications

In the course of image processing,the collection,transformation and transmission of images are frequently affected by imaging equipments and noises in exterior environment, therefore,image quality declines.Because noises have big infection to the continuous processing of images,it has very important practical meaning to noises reduction. Wavelet analysis is a new international research field and along with continuous perfection of wavelet theory,it is also widely used in image denoising field.The development of wavelet analysis and its application situation in image processing are firstly introduced.Then,wavelet image denoising methods which are often used at present are described in detail.These algorithms are compared and the principle, characteristic and disadvantage of each algorithm are also analyzed.This paper chooses wavelet analysis-this new mathematic tool and on the basis of research on wavelet theory and image denoising algorithms,three new wavelet denoising methods are proposed.They are adaptive soft threshold image de-noising based on stationary wavelet transform and neighbor dependency,wavelet shrinkage image de-noising method based on scales consistency edge detection and neighbor dependency and wavelet packet image denoising method based on context model.The first method fully considered dependency relation among scales and within scales wavelet coefficients. The information redundancy of stationary wavelet transform is also used to reduce noises. The second method emphasized edge preservation while eliminating noises.The edge pixels and non-edge pixels which are detected are treated differently and disposed in different methods.That is to say different shrinkage factors are used on them.While the third method made use of the more elaborated decomposition of wavelet packet and through context model,each wavelet packet coefficient's edge variance is estimated and then,threshold is adaptively adjusted.The denoising effects are satisfied.At last,through simulation experiments,the availabilities of these three new denoising methods are validated.