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实现信号希尔伯特变换，以及小波变换，小波变换要求数据长度是2的N次幂(The signal Hilbert transform and wavelet transform, wavelet transform requires that the data length 2 is N th)

二代小波的labview实现，详细介绍在labview中如何实现二代小波(Labview implementation of second generation wavelet)

说明：  基本的S变换matlab实现 用于时频分析 为初学者提供基本代码(S transformation for time-frequency analysis to provide basic code for beginners)

LeGall(5，3)小波滤波器，其计算复杂度小、存储开销低、压缩效率高，已被JPEG2000标准采用。提升小波实例c语言实现(Lifting Wavelet Example C Language Implementation)

说明：  图像小波变换，小波变换（wavelet transform，WT）是一种新的变换分析方法，它继承和发展了短时傅立叶变换局部化的思想，同时又克服了窗口大小不随频率变化等缺点，能够提供一个随频率改变的“时间-频率”窗口，是进行信号时频分析和处理的理想工具。它的主要特点是通过变换能够充分突出问题某些方面的特征，能对时间（空间）频率的局部化分析，通过伸缩平移运算对信号（函数)逐步进行多尺度细化，最终达到高频处时间细分，低频处频率细分，能自动适应时频信号分析的要求，从而可聚焦到信号的任意细节，解决了Fourier变换的困难问题，成为继Fourier变换以来在科学方法上的重大突破。(Image Wavelet Transform)

对信号进行小波变换，抑制系数小波滤波，采用了硬阈值滤波方法(right signal wavelet transform, wavelet filter coefficients inhibit use of the hard threshold filtering method)

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：  基于小波的图像分解与重构以及各种灰度图像。(Wavelet-based image decomposition and reconstruction, and various gray scale images.)

一维小波单支重构函数，一维小波系数直接重构函数(Single one-dimensional reconstruction of wavelet functions, the direct reconstruction of one-dimensional wavelet function)

说明：  真正的3 d双树离散小波变换(DWT)的一个数据集x是使用四个严格采样实现可分3 d DWTs在平行。然后subbands四DWTs结合适当。 (The real the 3 d Doubletree discrete wavelet transform (DWT) data set x four to strict sampling separable 3 d the DWTs In parallel. The then the subbands four DWTs combined appropriately.)