matlab3

说明：  个比较经典的二维经验模式分解程序代码，适用于图像分解，加油(Comparison the classical two-dimensional empirical mode decomposition code for the image decomposition, refueling)

说明：  基于dct系数的数字水印算法实现，在matlab环境中实现(coefficient based on digital watermarking algorithm in Matlab environment to achieve)

小波神经网络,实现曲线拟和，数据分类，故障诊断，学习速度快于传统BP。(Wavelet neural network, and realize curve fitting, data classification, fault diagnosis, learning faster than the traditional BP.)

超声图像去噪，基于小波变换的通用超声图像去噪，能够有效的去除相干斑(Ultrasound image denoising based on wavelet transform common ultrasound image denoising, can effectively remove speckle)

对地震数据进行gabor反褶积处理，有实例验证方法，简单易懂(The seismic data are processed by Gabor deconvolution.There are examples of methods to verify, easy to understand)

小波分解，得到低频系数并求特征熵，可用来对信号进行分选识别(Wavelet decomposition, low coefficient and entropy, which can be used for signal sorting and recognition)

基于matlab的小波包分解应用于机械振动信号的故障分析(Used in failure analysis of mechanical vibration signals based on the Matlab wavelet packet decomposition)

小波阈值法去噪的程序，其中有关于信噪比和均方根误差的公式(wavelet denoising)

single level ,two level, multi level dwt for the denoising

在理解了离散小波变换的基本原理和算法的基础上，通过设计VC程序对简单的一维信 号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波和Haar小波变换，从而得到小波分解系数；再通过改变分解得到的各层高频系数进行信号的小波重构达到消噪的目的。在这一程序实现的过程中能直观地理解信号小波分解重构的过程和在信号消噪中的重要作用，以及在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。(In understanding the basis of discrete wavelet transform basic principles and algorithms, through the design VC program Daubechies wavelet, Morlet wavelet and Haar wavelet transform after a simple one-dimensional signal plus a Gaussian white noise, resulting wavelet coefficients wavelet reconstruction signal again by changing the layers to achieve high-frequency coefficients decomposed noise cancellation purposes. Can intuitively understand wavelet decomposition and reconstruction process and an important role in signal de-noising in the process of implementation of this program, and in the high-frequency coefficients when the weight of the layers selected for signal processing coefficients denoising affected.)