Gabor小波提取特征

这是三维离散小波变换程序，里面附带程序说明，及实例讲解。(This is a three-dimensional discrete wavelet transform, which comes with the program instructions and examples to explain.)

在labview中进行小波分析程序设计，内容最全、最好的文件，参照本文件可以直接一步步编写小波分析程序，适合初学者。(The labview wavelet analysis program design, the most comprehensive, best file reference in this document can step directly Step preparation of a wavelet analysis program for beginners.)

小波变换、EMD以及EMD结合小波变换，几种常用的时频去噪方法在脑电预处理中运用的的对比分析，算法过程解释详细，有利于初学者入门学习；小波变换、EMD以及EMD结合小波变换，几种常用的时频去噪方法在脑电预处理中运用的的对比分析，算法过程解释详细，有利于初学者入门学习

OFDM系统完整程序，小波/小波包变换在OFDM中的应用的程序。(Complete program of the OFDM system, wavelet/wavelet packet transform in OFDM applications program.)

实现离散余弦(TCD)变换(achieve discrete cosine (TCD) Transform)

利用小波包变换对信号进行分解及重构，对于初学者是一个很好的学习材料。(Wavelet packet transform signal decomposition and reconstruction, is a good learning materials for beginners.)

二维连续小波变换，以及高维连续小波变化，简单易懂！！(Mainly includes two dimensional continuous wavelet transform and higher dimensional continuous wavelet transform and its application!!)

说明：  CEEMDAN和EEMD等去噪方法的合集(CEEMDAN/EEMD/EMD:Collection of various denoising methods)

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

说明：  可用于信号的EMD、EEMD、VMD分解，里面都加了自己的注释，通俗易懂(It can be used for signal EMD, EEMD, VMD decomposition. Because the algorithm adds its own annotations, it's easy to understand)