图像的小波降噪.doc

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1、图像的小波降噪 图像的小波降噪 摘要：图像降噪1直是图像处理领域1个研究得比较多的课题，也是1个热点领域。其中小波变换降噪技术是被研究的最多1种技术，本文主要讨论近几年兴起的阈值降噪技术。2维小波分析用于图像降噪的步骤如下。   （1）2维图像信号的小波分解。在这1步，应当选择合适的小波和恰当的分解层次（记为N），然后对待分析的2维图像信号进行N层分解计算。（2）对分解后的高频系数进行阈值量化。对于分解的每1层，选择1个恰当的阈值，并对该层高频系数进行软阈值量化处理。（3）2维图像信号的小波重构。同样的，根据小波分解后的第N层的近似（低频系数）和经过阈值量化处理后

2、的各层细节（高频系数），来计算2维信号的小波重构。还介绍了小波的数学基础。如：小波变换，小波离散及框架，多分辨率分析和Mallat算法的信号分解和重建过程。图像信号的小波降噪步骤和1维信号的降噪步骤完全相同，所不同的是，处理工具是用2维小波分析工具代替了1维小波分析工具。利用MATLAB 7 ，通过具体的例子来说明如何利用小波分析进行图像降噪这个问题。   关键字：图像降噪；小波分解；阈值量化；小波重构Denoising Image by Using Wavelet Abstract：Image noise reduction has been an area o

3、f image processing more research topics, as well as a hot field. Wavelet transform noise suppression technology is a study of the most technical, In this paper, we mainly discusses the noise suppression technology of noise threshold which is a method rising in recent years. Wavelet analysis for the

4、two-dimensional image noise reduction steps are as follows.(1) The wavelet decomposition of two-dimensional image. In this step, we should choose a suitable and appropriate wavelet decomposition levels (recorded as N), then decompose the 2-D analyzed image signal into N layer decomposition. (2) Thre

5、shold Quantified about the high-frequency coefficients decomposed. For each level of decomposition, we choice an appropriate threshold, and decide the quantity of the soft threshold for high-frequency coefficients of this layer. (3) The reconstruction of two-dimensional image signal by using wavelet

6、. Similarly, according to the approximation of the Nth level (coefficient of low frequency) decomposed by using wavelet and the various details (coefficient of high-frequency) after quantified for the threshold values, calculate the wavelet reconstruction for the two-dimensional signal.The mathemati

7、cal base of wavelet also is introduced, such as: wavelets transformation, discrete wavelet and framework, multi-resolution analysis, Mallat algorithm for the process of decomposition and reconstruction of a signal.The steps of noise reduction by using wavelet for image signal are identical to the st

8、eps of one-dimensional signal noise reduction. The only difference is the process tools. It is using two-dimensional wavelet analysis tools instead of one-dimensional wavelet analysis tools. By using MATLAB 7, through specific examples illustrate how to use wavelet analysis to denoise for an image.Keywords： image noise reduction ( denoise of a image); decomposition applying wavelet; quantization of a threshold、reconstruction by using wavelet