tezhengtiqu

说明：  小波变换技术与工程实践：图像的小波变换(MFC框架下)(Wavelet transform technology and engineering practice: Image Wavelet Transform (MFC framework))

小波变换测试，小波分解的级数设置为3级，可以实现数字图像的小波变化和逆变化。测试代码请使用512*512灰度图像(The wavelet transformation test, wavelet decomposition series is set to 3, digital image wavelet changes and reverse changes.)

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图像滤波变换程序，上学期课程设计代码，绝对原创(Image filtering transform procedures, last semester curriculum design code, an absolute original)

计算小波时间熵的源程序，内容详尽，调试正确，非常有用。计算小波时间熵的源程序，内容详尽，调试正确，非常有用。(Calculate the entropy of the source wavelet time, detailed, debugging is correct, very useful. Calculate the entropy of the source wavelet time, detailed, debugging is correct, very useful.)

说明：  小波变换去噪方法，过滤高频低频波，C++代码(Wavelet transform denoising method, filtering high frequency low frequency wave, C ++ code)

说明：  用MATLAB编程实现实现滤波，与数字信号处理相关(Implementing filtering)

小波方法进行降噪，阈值函数来完成不同设计，核心程序如下面的所示，使用的小波基有coif5，db3，sym4，bior2.4，haar，rbio(Wavelet method for noise reduction, design to complete different threshold function, the core program as shown below, using the wavelet base coif5, db3, sym4, bior2.4, haar, rbio )

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)