svd_dwt

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.)

小波信号分解与重构的Matlab程序，程序里边涉及卷积的直接调用的Matlab的conv()函数。(Wavelet signal decomposition and reconstruction in Matlab,Program inside invoked directly related to the convolution of Matlab conv () function.)

single level ,two level, multi level dwt for the denoising

故障行波波头识别：模极大值原理识别故障行波波头(Modulus maxima principles identified wave head)

利用小波变换实现和直方图均衡化相对比的图像的增强与分割.应该比较有用.分割也采取了几种技术的对比(using wavelet transform and histogram equalization relative ratio of image enhancement and segmentation. Be more useful. Pm cutting has taken several technical contrast)

基于小波变换的去雾处理程序，附有实验图片，可以运行的，希望对大家有帮助！(Based on wavelet transform to fog handler, attached to the experimental images, you can run, I hope it can help you!)

对图像进行小波变换的分解和重构的代码，包含以为信号的处理。( The image decomposition and reconstruction of wavelet transform code, including that the processing of the signal.)

很好的ica算法，计算ica时候用到，很好参考价值，卡内基学院用的程序(Good ica algorithm to calculate ica when used, a good reference value, the Carnegie Institute program)

在国外的好论坛下的，gabor小波变换程序，已测，放心使用，非常好的程序。还有一个gabor的也在我的上载中(Good forum in a foreign country under, gabor wavelet transform procedures, has been measured, ease of use, very good program. There is also a gabor' s also my upload)

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