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相关性小波去噪的程序实现，相关性去噪的程序实现(Wavelet denoising procedures related implementation procedures related to implementation of denoising)

说明：  小波标架的共轭梯度算法（Mallat，信号处理的小波导引）(Conjugate gradient algorithm for computing daul function.)

基于morlet小波的小波熵的源代码，希望有用，共分享(the code of waveletentropy)

此程序仿真了一个被污染了的信号源，然后通过小波去噪，成功取出了信号中的杂波(This procedure simulation has polluted the supply oscillator, then through the wavelet denoising, the success took out in the signal clutter )

说明：  紧支撑小波基的构造。 以Daubechies小波滤波器系数为例，推导出小波函数及尺度函数，并画出其图像. void Convolution(double* pShortData, int nShort, double* pLongData, int nLong) int CalcScaleFunction(double* pOutData) int CalcWaveletFunction(double* pOutData) Convolution()不是通用的离散卷积代码，而是简单地实现了后面两个函数需要的离散卷积，它要求输入的前一个数组长度小于后一数组长度，而且卷积结果就直接覆盖存储在后一数组中。CalcScaleFunction()计算尺度函数，CalcWaveletFunction()计算小波函数。 (Compactly supported wavelet bases constructed. To Daubechies wavelet filter coefficients as an example, the derived wavelet function and scaling function, and draw their images. Void Convolution (double* pShortData, int nShort, double* pLongData, int nLong) int CalcScaleFunction (double* pOutData) int CalcWaveletFunction ( double* pOutData) Convolution () instead of generic discrete convolution code, but simply the realization of a function behind the need for two discrete convolution, which requires input before an array length is less than the array length of the latter, but the result of convolution directly covered by the latter is stored in the array. CalcScaleFunction () calculated scaling function, CalcWaveletFunction () calculation of wavelet function.)

小波变换，小波去噪，包含多种去噪方法，模极大值，自适应，阈值去噪(Wavelet transform, wavelet denoising, including multiple Denoising, modulus maxima, adaptive thresholding)

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.)

用matlab编写找到向量维度的波峰波谷的值.(Prepared using matlab to find vector value of the dimension of the peaks and valleys.)

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

采用小波变换的方法实现心电信号的滤波，分别使用了硬阈值、软阈值和改进阈值方法，并实现了滤波效果的评价（均方差和信噪比）(Wavelet transform of ECG signal filtering method, respectively, using a hard threshold and soft threshold and improve the threshold method, and to achieve the filtering effect of the evaluation (both variance and signal to noise ratio))