wave_denoise

该文讲解了分数阶微积分和小波变换之间的关系，很不错的论文(The article explained the fractional calculus and the relationship between the wavelet transform, a very good paper)

第二代小波变换，包含常见的小波基函数和算例(The second generation wavelet transform contains the common wavelet basis functions and examples)

说明：  CEEMDAN和EEMD等去噪方法的合集(CEEMDAN/EEMD/EMD:Collection of various denoising methods)

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.)

AR过程的线性建模的Burg算法参数估计m文件(Burg method for AR processing)

9/7 二维整数小波变换，可以实现图像的无损恢复。(9/7 two-dimensional integer wavelet transform, you can realize non-destructive restoration of images.)

运行emd,得到输入信号X的固有模态函数（imf），每列代表着信号X的一阶分量，X就是我们采集的信号。 hhtdn是hht去噪的程序，可以不看，仅看emd 和 findpeaks wvtdn是wavelet去噪程序，如果只是分解信号，仅用下面的语句即可 [C,L] = wavedec(x_noise,dl,wn); x_noise就是我们采集的信号，dl是分解的阶数，wn是分解的母小波名称(% Empiricial Mode Decomposition (Hilbert-Huang Transform) % imf = emd(x) % Func : findpeaks)

小波包和EMD方法对信号分析后的频谱图比较，可以对EMD的运行参数进行修改。(Spectrum comparison of Wavelet package and EMD)

说明：  针对不同采样频率和输入信噪比的心磁信号进行滤波操作，呈现出对同一输入信号采取不同滤波算法的去噪结果。(The filtering operation is performed on the magnetic heart signals of different sampling frequencies and input signal-to-noise ratios, showing the denoising results of different filtering algorithms for the same input signal.)

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