基于二维小波变换的图像分解与重构技术研究

Xin Zhang, Ren-jin Zhang
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引用次数: 11

摘要

小波是近十年来发展起来并迅速应用于图像处理和语音分析许多领域的一种数学工具,是继傅立叶(Joseph Fourier)分析之后的又一突破。通过小波变换分析数字信号的局部特征,可以对所依赖的尺度进行拉伸和位移变换,进而显示出数字信号内部的本质特征。首先介绍了小波变换算法的原理,然后利用二维离散小波变换对图像进行分解和重构,并利用MATLAB软件环境进行了实验。实验结果充分肯定了小波变换图像处理能力的预期结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The technology research in decomposition and reconstruction of image based on two-dimensional wavelet transform
Wavelet is the last decade developed and quickly applied to a mathematical tool in many areas of image processing and speech analysis is a new breakthrough following Fourier (Joseph Fourier) analysis. Wavelet transform analysis of the local characteristics of the digital signal can depend on the scale stretching and displacement transformation, and then show the essential characteristics of digital signals within the. Firstly, the principle of the wavelet transform algorithm, and then use the two-dimensional discrete wavelet transform image decomposition and reconstruction, and experiments carried out by using MATLAB software environment. The desired results of expe-riments on wavelet transform image processing capability has been fully affirmed.
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