文档介绍：摘要
随着信息时代的快速发展,人们对数字图像的质量要求越来越高。但是数字图像在采集和传输过程中,图像经常受到各种噪声影响,所以要对图像进行处理。随着小波理论的不断完善,小波在图像降噪中也得到了广泛的应用,因此图像降噪具有很强的理论意义和应用价值。
小波域降噪是根据信号和噪声在小波变换下表现的方式不同,构造出相应的规则,把噪声产生的系数减小以至完全滤除,同时最大限度的保留有效信号。
本文主要研究基于小波变换在图像降噪中的应用,并得出一些结果。本文的主要工作是:首先简述了小波分析的发展、图像降噪的方法,并提出小波变换应用在图像降噪中的原因,以及小波图像降噪的必要性,小波图像降噪的发展方向;然后阐述小波分析的基本理论,其中包括小波变换理论、多分辨率分析以及图像的小波变换理论,这是小波图像降噪的基本理论;接着研究小波收缩阈值降噪,其中主要研究小波函数的选择和小波阈值的选取,并通过仿真实验说明这些对小波图像降噪的影响;最后介绍了维纳滤波器,把小波阈值法和维纳滤波器相结合,提出一种更好的降噪方法,通过实验仿真,比较小波阈值降噪和改进的维纳滤波器降噪的效果,进而得出结论。

关键词: 图像降噪小波变换小波函数阈值函数维纳滤波器
Abstract
With the increasing popularity puter in the information age, the quality desire of digital images es more and more strict. But digital image is usually corrupted by the various noise in its acquisition or transmission. So the noise needs to be reduced in the image processing. Recently, with the improvement of wavelet theory, wavelet transform in image denoising has been widely used, meanwhile, the image denoising has a strong theoretical significance and applied value.
Wavelet shrinkage is a method that creating certain regulation according to the different representation of signal and noise in wavelet domain and processing the wavelet coefficients. The essential lies in shrinking or deleting the coefficients raised from noise and reserve those raised from signal.
This article mainly study image denoising methods based on wavelet transform. A series of results are obtained from the research of the image denoising. The main work is : First, we outline the development of wavelet analysis, image denoise method, and put forward the reason of the application of wavelet transform in image denoising, and the necessity of wavelet image denoising, the direction of development in wavelet image denoising. Second, the fundamental theories of wavelet analysis are discussed in detail, we introduce the theories which include wavelet transform, multi-resolution analysis, wavelet function and wavelet transform of image. The contents of the chapter are the th