3D

说明：  关于小波变换的ppt，有用的可以下载看看(Ppt on Wavelet Transform)

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.)

Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing

EEG signal classification using wavelet feature extraction

使用小波包变换分析信号的MATLAB程序，利用小波包变换实现对故障信号的分析(Single analysis using wavelet package transforming)

实现小波变换，小波滤波器，小波除噪等功能。(To achieve wavelet transform, wavelet filters, wavelet denoising function)

基于小波变换的通信信号码元速率估计.pdf(Estimated rate based on the communication signal element wavelet transform. Pdf)

二次小波变换的方法用于数字信号码元速率估计(wavelet transform for bitrate estimation of digital modulated signal)

wavelet transforms in advanced forms

说明：  很详细的hht编程代码，matlab亲测可用，提供了比较清晰的注释，帮助理解(Very detailed HHT programming code, matlab pro test available, provides a relatively clear note to help understand)