wavelet_demo

NormThresh of wavelet coefficient

本代码是小波包学习和使用的初学代码，简单改造可对信号进行滤波，去噪，实现我们需要的各种功能(xiaobobao )

关于小波信号处理中的Morlet算法的Matlab程序(On the Morlet wavelet signal processing algorithms Matlab program)

移动窗口二维相关光谱方法matlab的实现，完全是自己参照公式编的哦。。移动窗口二维相关光谱是一种新的二维相关分析方法,它将移动窗口的概念和二维相关分析方法有效地结合在了一起,利用移动窗口将庞大的光谱数据按矩阵分割成若干个便于操作的子矩阵,用二维相关光谱分别处理,将得到的结果综合分析,用以光谱变量和扰动变量为坐标的等高图表现出来,从中可以非常直观地观察出光谱强度在扰动变量方向上的变化,进而找出引起光谱强度突变的特征扰动点。(Moving window two-dimensional correlation spectroscopy method matlab realization, is entirely their own formulas reference the Oh. . Moving window two-dimensional correlation spectroscopy is a new two-dimensional correlation analysis method, it will move the window concept and two-dimensional correlation analysis method is effective to combine together, using the moving window at the huge spectral data matrix is ​ ​ divided into a number of easy to operate a sub-matrix, respectively, using two-dimensional correlation spectroscopy process, comprehensive analysis of the results obtained for the spectral variables, and disturbance variables manifested contour map coordinates, which can visually observe the spectral intensity of the direction of the disturbance variable The change, then find mutations characteristic spectral intensity caused disturbance point.)

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)

小波变换在图像处理中应用于图像分析，图像压缩，图像去噪等。本文就小波变换理论和matlab语言的小波分析工具箱做了简单阐述。(Wavelet transform in image processing applied to image analysis, image compression, image denoising, etc.. In this paper, wavelet transform theory and wavelet analysis matlab language to do the simple toolbox on.)

说明：  使用小波变换和神经网络的混合图像压缩，希望对大家有所帮助(Using wavelet transform and neural network hybrid image compression, I hope all of you to help)

基于小波变换的图像编码，已经使用，效果很好。(Image coding based on wavelet transform has been used with good results.)

该程序主要是通过小波变换来实现对心音信号的去噪处理(The program is mainly achieved by wavelet denoising of heart sound signals)

《小波十讲（修订版）》主要内容简介：原书《小波十讲》（ten lectures on wavelets）是一本世界范围公认的经典学术名著，是当代数学著作中一本影响巨大的绝妙好书。(text book for wavelet analysis)