论文编号:XXLW111 论文字数:7186,页数:28

摘     要
 
 随着人类社会的发展，人类文明的进步，人们所掌握的知识也越来越多。一些之前不为多数人所熟知的科学知识和科学技术也慢慢的开始被人民所接受和了解。近些年小波在信号处理、图像处理、语音与图像编码、语音识别与合成、多尺度边缘提取和重建、分形上的优点使得小波分析理论受到众多学科的共同关注，而医用图像学的发展又促使小波与医学的相互结合。因此本文通过对二维小波的基本图像分解和图像重组的原理叙述和介绍，在选择好合适的原始图像情况下确定合适的小波分析系数，再联系二维小波分析法对医学图像的图像压缩，图像消噪，图像增强，图像平滑处理，以及图像融合处理等方面的实际应用的研究，用实际操作来证明小波在医用图像处理中的应用的可行性。
 
关键词：图像压缩 图像消噪 图像增强 图像平滑处理  图像融合

Abstract
 With the development of human society and the advance of human civilization, people have more and more knowledge to master. Some scientific knowledge and technology that no well-known before to most people are also slowly began to be accepted and understanding. In recent years wavelet theory has been interested by a broad range of subjects because it’s advantage in signal processing, image processing, voice and image coding, speech recognition and synthesis, multi-scale edge extraction and reconstruction, what’s more, the development of Medical Image promote the wavelet and medicine to be combined. In this paper, the author introduce the principle of two-dimensional wavelet base image decomposition and the image re-organized. under condition of choose right primitive image ascertains we choose the right wavelet analysis modulus. The author also has done research on image compression, image elimination, image strengthen, image smooth Handled and integration of image processing by Two-dimensional wavelet analysis, through the actual operation to prove feasibility of application that wavelet in the medical image processing.

Keywords：Image compression; Image elimination; Image strengthen; Image smooth; Handled    Image fuses
 
目  录
摘     要 I
Abstract II
目  录 III
第一章  绪论 1
1.1  引言 1
1.2  原理 2
第二章  二维小波的原理概述 4
2.1  Mallat算法的信号分解过程 4
2.2  Mallat算法的信号重建过程 5
2.3  二维小波变换 6
第三章  二维小波分析在医用图像处理上的应用 8
3.1  原始图像描述及二维小波系数选择 8
3.2  二维小波分析具体应用 12
3.2.1 图像压缩 12
3.2.2 图像降噪 13
3.2.3 图像增强 15
3.2.4 图像平滑处理 16
3.2.5 图像融合处理 16
3.3  分析结论 17
第四章  结论 19
致 谢 20
参考文献 21
附 录 22

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