【作者】 曾诚；

【导师】 黄朝兵；

【作者基本信息】 武汉理工大学 ， 通信与信息系统， 2008， 硕士

【摘要】 小波分析在图像处理中有非常重要的应用。本文研究方向是在其图像去噪和图像增强中的应用。小波分析是傅立叶分析思想方法的发展与延拓。二维小波分析用于图像去噪和图像增强是小波分析应用的一个重要方面。小波分析用于图像去噪和增强具有明显的优点。基于小波分析的图像去噪方法有很多,比较成功的有小波阈值法,空域相关法,模极大值重构法,投影法等。而基于小波的图像增强方法也比传统图像增强方法更有效。主要工作包括:本文详细阐述了小波基本理论在图像处理中的应用,介绍了连续小波变换和离散小波变换,给出离散二进小波变换的快速分解与重构算法,最后研究了小波基的函数及其特性,分析了它们对实际应用的影响和作用。在对目前小波理论、小波图像去噪的相关文献进行研究的基础上,介绍了小波变换在图像去噪领域的应用;其次,对目前常用的几类小波去噪方法进行了分别阐述,着重分析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Matlab进行仿真实验,分别对含噪图像使用改进的阈值,改进的阈值函数进行去噪处理,新函数是现有软、硬阈值函数的推广,通过调整参数,克服了硬阈值函数不连续和软阈值函数有偏差的缺点,改善了图像的视觉效果和客观指标,对图像进行仿真实验得到了较好的结果。研究了基于小波变换的图像增强,先分析了图像增强的基本方法,然后将图像增强放入小波域中去研究,并提出了一种新的小波变换自适应图像增强算法,在Matlab环境中验证了该算法的可行性和优越性。本文主要研究了基于小波的图像去噪与增强技术的理论基础,提出了新的基于小波变换的图像去噪和增强方法,以Matlab为平台实现图像去噪和增强算法过程,并对相应的图像处理结果进行了分析和比较,验证了其可行性和高效性。更多还原

【Abstract】 Wavelet analysis in the image processing applications are very important. In this paper, the direction of its image noise reduction and image enhancement are researched in the application. Wavelet analysis is the development of Fourier analysis of the way of thinking and extension. Two-dimensional images of wavelet analysis for noise reduction and image enhancement application of wavelet analysis is an important aspect. Wavelet analysis for noise reduction and enhance the image has obvious advantages. Based on wavelet analysis of the image means a lot of noise, the more successful a wavelet threshold method, air-related laws, modulus maximum reconstruction, projection and so on. And the wavelet-based image enhancement methods than the traditional image enhancement methods more effective.The fundamental theories of wavelet analysis are discussed in detail. Continuous wavelet transform, discrete wavelet transfonn and dyadic wavelet transform are introduced. The fast algorithm of discrete dyadic wavelet transform is given. Finally, an analysis is made on the influence of the wavelet bases on practical applications by studying their mathematical properties.Based on the profound comprehension and generalization of a lot of existing literature on wavelet de-noising, the applications in the image de-noising are described; second, the wavelet image de-noising methods are classified and introduced, moreover, the shortcomings of threshold de-noising method are specially analyzed; in the end, after analyzing and comparing the classical threshold de-noising methods, a new threshold selecting method and a new threshold function are proposed. Simulation experiments are implemented by Matlab wavelet tool box. And experiment results show that the new methods usually obtain better performance than classical threshold de-noising methods.Image enhancement based on wavelet transform are studied in the thesis. Firstly, the concept of wavelet transform is described, and the characters after wavelet transform are analyzed; secondly, an adaptive local threshold scheme is proposed namely by threshold of the wavelet transform; At last, the way that wavelet cooperate with image enhancement realize the arithmetic, which balance de-noising and image enhancement. The Matlab simulation experiment indicates the new adaptive image enhancement is effective and excellent.This paper elaborated on the wavelet-based image noise reduction and enhancement of the theoretical basis to Matlab as a platform for noise reduction and image enhancement algorithm to achieve process and the corresponding image processing results were analyzed and compared in order to arrive at a comprehensive performance distinctions based on wavelet of the noise reduction and image enhancement algorithm program.更多还原