摘要图像在获取和传输阶段可能会受到噪声的影响，图像去噪是图像处理应用的一个必要的步骤，其目的是尽量保留原信号的有效信息，同时去除噪声信号。经过多年的研究，图像去噪技术日趋完善，其拥有着广泛的应用。大多数的降噪方法，在本质上，是通过使用低通滤波去除噪声的。使用这些方法去噪时会去除图像高频信息中的有用的部分，导致图像的边缘失真和图像纹理细节模糊。小波变换在时间域和频率域中同时拥有良好的局部特性，从而非常适合时变信号的分析和处理，特别是在图像去噪研究中，拥有良好的应用前景。83816
本论文使用小波变换对图像进行去噪研究，使用小波函数对含噪图像进行分解，再使用阈值函数对小波系数阈值化处理并重构图像。给出相应的小波变换去噪算法实例，并和传统的均值滤波以及维纳滤波图像去噪算法进行仿真效果对比。使用Matlab工具箱提供的小波函数对含噪图像进行分解重构，对分解后得到的各层小波系数进行阈值化处理，在图像重构时能够有效的去除噪声，较好的保留图像本身的细节纹理和边缘信息，在图像去噪和视觉效果之间达到良好的平衡。
毕业论文关键词：小波变换；图像去噪；小波阈值
Abstract In the acquisition and transmission phase,image may be subject to noise effects, image denoising is the image processing application of a necessary step, its purpose is try to keep the effective information of the original signal, while removing the noise signal。 After many years of research, the image denoising technology is becoming more and more perfect, which has a wide range of applications。 Most of the noise reduction methods, in essence, are by using low pass filtering to remove the noise。 Using these methods to remove the high frequency information in the image of the useful part, resulting in image edge distortion and image texture detail fuzzy。 Wavelet transform has good local characteristics in time and frequency domain, which is very suitable for the analysis and processing of time-varying signals, especially in the research of image denoising, has a good application prospects。
In this paper, wavelet transform is used to remove the image denoising, the wavelet function is used to decompose the noisy image, and then the threshold function is used to deal with the wavelet coefficients and reconstruct the image。 The corresponding wavelet transform denoising algorithm is given, and the simulation results are compared with the traditional mean filter and Wiener filter image denoising algorithm。 Provided by the use of Matlab toolbox wavelet function of noisy image of decomposition and reconstruction, thresholds the decomposition of the wavelet coefficients。 In the image reconstruction can effectively remove noise, preserve the image detail texture and edge information in images to achieves a good balance between noise and visual effects。
Keywords: Wavelet transform; Image denoising; Wavelet threshold
目 录
第一章 绪论 1
1。1。 研究背景 1
1。3。 小波理论简介和发展 2
1。4。 主要研究内容 2
第二章 均值滤波及维纳滤波图像去噪方法 4
2。1。 均值滤波简介 4
2。2。 均值滤波算法 4
2。3。 维纳滤波简介 5
2。4。 维纳滤波算法 Matlab小波变换的图像去噪算法与实现:http://www.youerw.com/renwushu/lunwen_98995.html