基于小波变换的图象去噪算法研究设计.doc

摘要：图像中总是存在有许许多多的噪声,为了更好地对图像进行分析,就必须在图像预处理中减小图像中的噪声。小波分析是一种有效的分析工具,近来随着小波理论的不断发展完善,小波理论已经被应用到各个学科领域。同样,小波在图像去噪中也得到了广泛的应用,并产生了许多小波图像去噪算法。 随着网络及计算机在人们生活中的日益普及，图像、音频等多种形式的多媒体文件极大地丰富了人们的生活。同时人们对于图像的画质要求也在不断提高，于是图像处理就提到了研究的日程上来。数字图像在采集和传输的过程中，经常会受到噪声影响，使图像发生降质。针对这一问题，各种各样的图像去噪方法被提出来。小波变换因其自身所具有的诸多优良特性，越来越多地被应用于图像去噪领域。其中，小波阂值去噪算法因为原理及实现简单，应用性能良好，近年来受到了国内外学者的广泛关注。在对目前小波理论、小波图像去噪的相关文献进行认真理解和深入研究的基础上，分析了现有方法的不足。本文的主要结构如下:首先，概括描述了小波理论及其优缺点;其次，引出小波变换在图像去噪领域的应用:再次，对目前常用的几类小波去噪方法进行了分别阐述，着重介绍了阈值收缩法并分析了其存在的不足;最后，提出了新的阈值选取方法和阈值函数改进方法。本文采用MALTAB进行仿真实验，首先分别对含噪图像使用改进的阈值，改进的阈值函数进行降噪处理，然后将两者结合起来应用于含噪图像。实验结果表明，使用改进后的阈值和阈值函数进行图像降噪，较之现有的经典方法，通常可获得更好的效果。 关键词：小波分析；小波；阈值；阈值函数；去噪 Based On Wavelet Transformation Image Denoising Algorithm Research Denoising Abstract：There is a lot of noise in images. In order to analyse images, the noise needs to be reduced in images pre-processing. Wavelet analysis is a valid analyzing means. Recently, with the development and improvement of wavelet theory, wavelet analysis has been applied to many fields. At the same time, wavelet theory has been applied to image denoising successfully,too, and many new image denoising algorithms is formed。 With internet and computer are used more and more today，the multi media Just like audio，videoetc.enrlches human’slife a lot.Mean while，the Require mento fimage qua1ity is ra1sing day by day herefore，it is necessary To do some researchon image Processing. Digital images usually corrupted by the noise in its acquisition or transmission，which egradethe qua1ity of the origina1 mage.In order to solve this problem and gain clearer images，many kinds of image denoising aroaches are proposed.Wavelet image denoising has been well acknowledged as an important image denoising method for its splendid characteristics. Especia1ly wavelet thresholding denoising method because of its simple principle and excellent Performance， researcher shave paid much more attention to it in the recent years. Based on the Profound comprehension angenera1