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Brown noise generated in Matlab

矢量区域填充算法（包括汉字提取生成） 算法(Vector area filling algorithm (including the extraction of Chinese characters generated) algorithm)

静态画图，可以改变图形边框颜色与矩形或椭圆的填充颜色(Static drawing, you can change the fill color graphics border color and rectangular or oval)

这是一个（原创的）用matlab做成的维纳滤波器程序，可以直接运行，对于初学数字信号处理的各位有不错的帮助。((original) using Matlab caused by Wiener filter procedures, and can directly run for the beginner in digital signal processing you have any good help.)

图像恢复：快速非局部均值滤波--A.Buwades等人提出的非局部均值滤波的快速实现方法(Image restoration: the proposed fast non-local means filtering- A. Buwades non-local means filtering Fast Implementation)

一篇基于Expected Patch Log Likelihood的图像去噪文章及其实现(One based on Expected Patch Log Likelihood Denoising article and its realization)

这是基于稀疏表示的图像重建，用来做图像去模糊或超分辨，绝对可以用(Sparse Representation for Image Restoration)

用来比较两张图片的相似程度的MATLAB程序(A MATLAB program used to compare the similarity between two pictures)

YUV Quantization using VS2010

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. )