DWTzip

说明：  地震勘探中小波变换，去噪，对初学者容易上手，运用maltlab编写，运行也方便，(Wavelet transform and denoising in seismic exploration)

wavelet transforms in advanced forms

EEG采集脑电波，然后用小波变换分析睁眼时产生脑电波的频率及各项数据(EEG brain waves collected and then analyzed using wavelet transform eyes when the frequency of brain waves generated and the data)

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.)

M. N. Do的经典的关于轮廓波变换的matlab代码，里面有matlab代码、论文、测试图片，很齐全，是很好的学习资料！(The MN Do classic on contourlet transform matlab code matlab code, papers, test image there, very complete, good learning materials.)

此为提供初学计算电磁学应用於色散介质的人，一维色散介质-Lorentz的程序(It is fortran code to provide a chance to learn the computational EM wave application of dispersive media for Lorentz model)

小波变换程序，用于对一维信号，二维信号进行稀疏变换(wavelet transform)

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：  多小波代码，有CL,GHM,Sa4,Opt-rec四种多小波的变换与反变化，前处理和后处理的代码。(Multiwavelet code, there are CL, GHM, Sa4, Opt-rec four Multiwavelet Transform and anti-change, pre-treatment and post-processing code.)

基于小波变换的语音识别，内含毕业论文和摘要以及可执行源程序(Speech recognition based on wavelet transform, containing thesis and summary of executable source code)