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提升方法实现小波变换，大大提高了小波变换的速度，并且不会降低图像恢复质量，很有价值的算法实现(upgrade method wavelet transform and greatly enhance the speed of wavelet transform, and will not reduce the image restoration quality, great value Algorithm)

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)

希尔伯特变换和小波变换检测相位差的matlab程序和一些资料(Hilbert transform and wavelet transform to detect the phase difference matlab program and some of the information)

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

与labview之间的联系 在小波变换方面(Contact between labview in terms of the wavelet transform)

心电信号分析 （1）设计滤波器，实现对心电信号的噪声抑制和基线纠漂。 （2）时域分析：R波检测算法与实现. （3）功率谱分析：对消噪后的信号进行功率谱分析。要求计算信号的功率谱，功率谱峰值，峰值频率。 (ECG Analysis (1) design filters to achieve noise suppression of ECG and baseline drift correction. (2) time-domain analysis: R-wave detection algorithm and implementation (3) The power spectrum analysis: noise cancellation signal after the power spectrum analysis. Require calculation of the signal power spectrum, peak power spectrum, peak frequency.)

5*5的中值旋滤波，以前在这下的旋滤波发现有点问题，改了一下(5* 5 median spin filtering, spin under the previous filter in this issue found that a little bit, changed a bit)

对仿真信号进行复morlet小波变换后，绘制的时频图。(After the simulated signal multiplexing morlet wavelet transform, time-frequency diagram drawn.)

用于图像的多尺度分析，shapelet变换用matlab 编程实现(shapelet code)

小波变换阈值去噪的文章，基于最大最小准则计算(Minimax Estimation, Adaptive Estimation, Nonparametric Regression, Density Estimation, Spatial Adaptation, Wavelet Orthonormal bases, BesovSpaces, Optimal Recovery.)