wavepage

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.)

基于小波变换的随机噪声降噪的matlab 主要运用的方法有软阈值去噪法和硬阈值去噪法(Based on wavelet transform matlab random noise and noise reduction methods are mainly used soft thresholding method and hard thresholding method)

基于小波变换的异步电机的故障诊断，本文采用三层小波包分析，诊断效果较好(Wavelet-based fault diagnosis of induction motors, this paper, three wavelet packet analysis, diagnosis, better)

关于小波变换的一些常用函数，有关多尺度变换，希望可以帮助您(Wavelet commonly used functions, on multi-scale transformation, I hope to help you)

这是本人的图像处理作业 包括利用各种基于小波的去噪过程，如硬阈值 软阈值等。。内容很丰富哦！相信一定对你有帮助的！(This is my image processing operations including the use of wavelet-based de-noising process, such as a hard threshold, such as soft-thresholding. . Oh, the content is very rich! I am sure will help you!)

基于墨西哥草帽小波函数进行连续小波变换的matlab变换程序，望对大家有所帮助。(Mexican hat wavelet function based on continuous wavelet transform matlab transformation process, hope to be helpful.)

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

用小波方法把脑电信号进行频段分解，并画出图形(Using wavelet method to decompose the eeg signals into frequency bands, and draw the graph)

自适应滤波器中的rls和lms两种算法matlab仿真比较程序。(Rls adaptive filter and the LMS algorithm matlab simulation comparison of two procedures.)

Wavelet compression algorithm. Start by computing a complete wavelet-transform using the cohen-daubechies-fevereau (2,2) biorthogonal wavelet (which incidentally seems to be the same as used for lossless JPEG2000, but they call it a (5,3)?).