Wienerfilter1

两个l1准则下的噪声干扰信号压缩感知重构举例，两个例子的稀疏矩阵均为DCT矩阵，而观测矩阵分别采用单位阵和随机矩阵，有详细的步骤和使用方法，适用于初步的学习压缩感知方法。(This programme supply two examples by Compressed sensing with l1 norm. The sparse matrix of two examples are all DCT matrix and the obsever matrix are unit matrix and random matrix respectively. We give the detailed usage and steps for the programme which is useful to the beginner.)

GDI实例3，绘制圆形、矩形等图形

双数复小波阈值除噪程序，可以实现双数复小波分解及重构。并能实现阈值除噪。(Double the number of complex wavelet threshold denoising procedures can achieve double the number of complex wavelet decomposition and reconstruction. And to achieve the threshold noise removal.)

采用最大方差阈值分割法，对指纹进行提取。(Using the maximum variance threshold segmentation method for fingerprint extraction.)

一个关于数据降维的工具包，外国牛人编写，囊括了各种降维方法，是一个很好的工具！(A tool for data reduction package, foreign cattle were written, include a variety of dimension reduction method is a good tool!)

本程序实现灰度图像各向异性扩散(Anisotropic Diffusion)滤波算法，可以用于平滑信号，保留边缘，增强边缘对比度。与普通高斯Gaussian算法相比，它不会平滑边缘的图像。(The program implements grayscale image using anisotropic diffusion filtering algorithm.It can be used to smooth the signal, keep the edge, enhance the edge of the contrast. Compared with ordinary Gaussian algorithm, it does not smooth the image edges.)

集成ITK、VTK、MFC读取DICOM的代码，建成win32程序即可。运行完美(Integrated ITK, VTK, MFC to read DICOM code, built win32 program can. Works perfect)

老外写的程序，可对三角形进行渲染，有详细的算法介绍。非常棒(A good Triangle Renderer Project，with detailed introduction.)

说明：  同态滤波利用去除乘性噪声(multiplicative noise)，可以同时增加对比度以及标准化亮度，借此达到图像增强的目的。(Homomorphic filtering can increase contrast and standardize brightness simultaneously by removing multiplicative noise, so as to achieve the purpose of image enhancement.)

斯坦福大学的姚邦鹏开发的一个图像分类算法，使用random forest实现了图像的精细区域描述，赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试，程序完全可运行，且提供了MAC和Windows下的两种程序。(Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our method is to identify the discriminative fine-grained image region that distinguishes different classes. To achieve this goal we sample image regions from dense sampling space and use a random forest algorithm with discriminative classifier. Each node of the tree of random forest is trained and tested with fine-grained image patches combining the information from upstream nodes together. We implemented each node of the tree with a discriminative SVM classifier, which makes the node as a strong classifier. )