ex001

此示意程序用DWT实现二维小波变换 1.调原始图像矩阵 2.进行二维小波分解 3.分解结果显示 重构源图像及结果显示 (This indicated the procedure using two-dimensional wavelet transform DWT realize one. Transferred the original image matrix 2. To carry out two-dimensional wavelet decomposition 3. Decomposition results show that reconstruction of the source images and results show)

一种小波分解，用于多分辨率分解，在分解的过程中，同时给出各级的细节和概貌。(a trous wavelet decomposition)

2002年，MN Do和Martin Vetterli提出了一种“真正”的图像二维表示方法：Contourlet变换，也称塔型方向滤波器组(Pyramidal Directional Filter Bank, PDFB)。Contourlet变换是利用拉普拉斯塔形分解(LP)和方向滤波器组(DFB)实现的另一种多分辨的、局域的、方向的图像表示方法。(In 2002, MN Do and Martin Vetterli proposed a "real" two-dimensional representation of images: Contourlet transform, also known as Pyramidal Directional Filter Bank (PDFB). Contourlet transform is another multi-resolution, local and directional image representation method based on Laplacian pyramid decomposition (LP) and directional filter banks (DFB).)

建立舰船航行的误差模型，通过进行卡尔曼滤波，求出测量曲线与真身曲线的误差曲线(Ship navigation error model established by the Kalman filter, find the measurement curve and the true identity of the error curve curve)

说明：  广义S变换及其逆变换代码，可以直接进行属性分析(Generalized S-transform and its inverse transform code can be used for attribute analysis directly)

说明：  基于dct系数的数字水印算法实现，在matlab环境中实现(coefficient based on digital watermarking algorithm in Matlab environment to achieve)

基于小波变换的图像去噪的matlab程序的源代码 基于小波变换的图像去噪的matlab程序的源代码(里面包含了源代码和运行的结果,(Image denoising based on wavelet transform of the matlab program s source code Image denoising based on wavelet transform of the matlab program s source code (which contains the source code and run the results of)

小波包分解和重构的过程，在齿轮箱中的应用(Wavelet packet decomposition and reconstruction of the signal in the application of matlab, and energy, frequency )

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

该程序主要是通过小波变换来实现对心音信号的去噪处理(The program is mainly achieved by wavelet denoising of heart sound signals)