MEAN_ratio

通过数字图像相关的方法计算两幅图像之间的位移，计算时间较长，是最基础的DIC，没有优化。(Digital image correlation method to calculate the displacement between the two images, the time is longer, is the most basic DIC, no optimization.)

SIFT 尺度不变特征提取算法 可参考可运行有测试图片(SIFT with test picture and it can work well)

基于surf算法的复制移动检测，此方法适合图像伪造研究者初级实验(Replication mobile detection based on surf algorithm)

首先通过多分辨率分解法提取高频边缘，然后分别对每层的高频边缘进行非线性插值得到新的高频边缘，接着对高频边缘进行高通滤波得到等强后的高频边缘，最后对增强后的高频边缘进行修正。再在对增强的图像进行对比度拉伸，从而达到增强的目的！(First of all, through the extraction of multi-resolution decomposition of high-frequency edge, and then were on the edge of each high-frequency non-linear interpolation to be the new high-frequency edge, and then the edge of the high-frequency high-pass filter to be strong, such as after the high-frequency edge, and finally on the brink of enhanced high-frequency be amended. Again in contrast enhanced image stretching, so as to achieve the purpose of enhancing!)

说明：  该工具箱可实现matlab编程和zemax通信，同时借助于matlab 强大的编程能力以及zemax的光学设计能力，为光学领域工作的科研人员提供了新的途径。(This program can realize the communication between the matlab and zemax, very helpful for optical engineer and student whose major in optical science.)

基于均值漂移的图像平滑MATLAB源戴码。(Based on mean-shift image smoothing Dai MATLAB source code.)

基于分块的人脸图像超分辨率学习算法实验平台，利用学习到的训练集图像信息对低分辨率图像超分辨率，并可以查看超分辨率后的图像块匹配结果（用鼠标双击结果显示窗口中的实验图像），可以设置实验参数。(Block-based face image super-resolution algorithm for the experimental platform to learn, learning to use the training set of low-resolution image information super-resolution images and can view the image super-resolution block-matching results (double-click with the mouse results display window of the experimental images), the experimental parameters can be set.)

说明：  详细介绍了多普勒天气雷达系统建模与仿真应用 (Details of the Doppler Weather Radar System Modeling and Simulation Application)

基于LEE滤波的极化SAR去噪 matlab实现(jiyu lee lvbo jihua SAR matlab)

说明：  LSA的第一步是要去创建词到标题（文档）的矩阵。在这个矩阵里，每一个索引词占据了一行，每一个标题占据一列。每一个单元（cell）包含了这个词出现在那个标题中的次数。例如，词”book”出现在T3中一次，出现在T4中一次，而”investing”在所有标题中都出现了一次。一般来说，在LSA中的矩阵会非常大而且会非常稀疏（大部分的单元都是0）。这是因为每个标题或者文档一般只包含所有词汇的一小部分。更复杂的LSA算法会利用这种稀疏性去改善空间和时间复杂度。(The Little Book of Common SenseInvesting: The Only Way to Guarantee Your Fair Share of StockMarket Returns ,)