compression

在实数域 ,对称、 正交的紧支集非平凡单小波基不存在 ,而多小波把紧支集，对称性、 正交性完美地结合在一起 ,使小波理论从标量扩展到矢量范畴。利用上述特点将多小波变换应用于 SAR 图像压缩。(In the real fields, symmetric, orthogonal set of nontrivial compactly supported wavelet basis does not exist alone, but more than the compact support wavelet, symmetry, orthogonality perfect combination, So wavelets theory extended Dao vector from scalar context. Use of these features will be more than SAR image compression wavelet transform.)

MATLAB实现kennard-stone选样本算法 (MATLAB kennard- stone selected sample algorithm)

说明：  给出了在matlab环境下的源代码，经过了上机调试，可以实现小波变换对图像的二次压缩！(given Matlab environment in the source code, after the Machine, can achieve a wavelet transform of the second image compression!)

数字全息和计算程式 计算全息图的制作和再现过程主要分为以下几个步骤： 1抽样。得到物体或波面在离散样点上的值。 2.计算。计算物光波在全息平面上的光场分布。 3.编码。把全息平面上光波的复振幅分布编码成全息图的透过率变化。 4.成图。在计算机控制下，将全息图的透过率变化在成图设备上成图。如果成图设备分辨率不够，再经光学缩版得到实用的全息图。 5.再现。这—步骤在本质上与光学全息图的再现没有区别。 (Digital Holography and CGH for calculating the production and reproduction process is divided into the following steps: 1 sampling. Be objects, or wavefront-like point in the discrete value. 2. Calculated. Calculation of complex light plane in the holographic optical field distribution. 3. Coding. The hologram plane the complex amplitude distribution of light into a hologram coding changes in the transmittance. 4. As Fig. Under the control of the computer, the hologram transmittance change in the mapping device mapping. Mapping equipment, if not resolution, then reduced by the optical version of a hologram to be practical. 5. Reproduction. This is- these in essence a hologram and the optical reproduction is no different.)

实现心电信号的去噪处理以及波形变化，提取信号周期，同时标记波形的起始点和结束点(Denoising ECG waveform changes, signal extraction cycle, at the same time mark waveform start and end points)

基于高阶累积量的MUSIC算法仿真，有直观结果可供参考(MUSIC algorithm simulation based on higher order cumulants, intuitive results available)

说明：  压缩包中为小波变换的程序，可在MATLAB 中使用，可进行时频分析(The compression package is a wavelet transform program, which can be used in MATLAB and can be used for time-frequency analysis)

几种小波阈值函数去噪的程序及其运行结果比较(Several wavelet threshold denoising function to run the program and its results more)

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：  利用小波变换去除图像中噪声，对比其他去噪方法(Using Wavelet Transform to Remove Noise from Image)