Image-Denoising-

局部投影去噪 自己编的 调试已经成功 拿来给大家借鉴 对象是混沌信号(Local projective denoising their own series of debugging has been successfully brought to everybody' s reference object is a chaotic signal)

纹理图像分割matlab程序，附带测试纹理图像图像，另外还有一篇高质量相关英文论文和一篇中文论文，供研究纹理分割的同学学习。(Texture image segmentation matlab, the supplementary test texture image image, in addition to a high quality related English papers and a Chinese paper, for research students texture segmentation study. )

稀疏自适应匹配追踪算法，无需稀疏度，就可以重构原始信号。(Sparse adaptive matching pursuit algorithm, without sparsity, we can reconstruct the original signal.)

个人编写的Matlab的红外图像增强程序，比较简单易懂！(Individual written Matlab infrared image enhancement procedure is relatively simple to understand!)

用matlab工具去除照片中水印，去除水印快速，简单，快速(Using Matlab tool to remove watermarks in photos is fastly, simplely.)

Z源逆变器开路三相SVPWM调制，注入了三次谐波分量。(Open-circuit Three-phase SVPWM Modulation of Z-source Inverter)

这个算法是基于峭度的快速不动点算法，混合方式是线性瞬时混合，适定情况，该算法收敛速度较梯度算法快，很实用。(failed to translate)

三维点云配准kd-tree搜索算法，用于特征点的搜索匹配(3 d point cloud registration kd- tree search algorithm, is used to search of feature point matching)

本算法在Matlab2008b 环境下实现。包括main,san 和ssd 三个函数。 这次实现的算法并不是比较两个已经知道的点是否匹配，而是已知一个图形中的特征点，在另外一个图像中找到与其最匹配的点，匹配度用SAD 或者SSD 来度量。 main.m 是程序的入口，包括生成和读入实验数据，调用sad 函数和ssd 函数求匹配点，最后绘 制出最后的结果。sad.m 是用sad 度量方法在另一图中求解匹配点的函数实现，有3 个参数 y=sad(x,image1,image2)。其中x 是代匹配的数据，image1 是图像1 数据，image2 是图像2 数据。 从理论上分析，ssd 比sad 算法要复杂一点，经过测试，在一幅640*480 的图像中寻找10 个匹配点数据SAD 用时25.062519 秒，SSD 用时25.291432 秒。(The algorithm Matlab2008b environment to achieve. Including the main, san, and ssd three functions. The implementation of the algorithm is not the point of comparing two matches already know, but the known feature points in a graphic, an image found in the other match with the most points, matching measured with the SAD or SSD. main.m is the entry, including the generation and reading test data, call the sad ssd function evaluation functions and matching points, and finally draw the final result. sad.m measure is sad figure in another match point in the function implementation to solve, there are three parameters y = sad (x, image1, image2). Where x is the generation of matching data, image1 is the data image 1, image2 is the image 2 data. From the theoretical analysis, ssd little more complicated than the sad algorithm, tested in a 640* 480 images of 10 match points in the search for data using time 25.062519 seconds SAD, SSD with time 25.291432 seconds. )

张正友标定法中计算内参的方法，经典原文资料(calculate Intrinsics zhang s method)