clahe

基于图像签名的显著性区域检测，算法很巧妙，好用(computation of a saliency map based on the Image Signature, as described in the following paper: "Image Signature: Highlighting sparse salient regions", Xiaodi Hou, Jonathan Harel, and Christof Koch, IEEE Trans. Pattern Anal. Mach. Intell. 34(1): 194-201 (2012))

这是用matlab编写的图像缩放代码，能实现灰度或者彩色图像的放大缩小以及小数倍。使图像处理初学者的好材料(This is used to prepare the image scaling matlab code, to achieve gray scale or color images and small many times zoom. Image processing to make a good material for beginners)

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. )

通过高斯核密度估计计算多元变量之间的互信息熵(The mutual information entropy between multivariate variables is calculated by Gaussian kernel density estimation)

视网膜血管分割的matlab版本算法，该算法分割较为细腻(Retinal blood vessel segmentation)

主要是用matlab进行图像增强对比度的处理，采用的方法是灰度拉伸(Is mainly used for image enhancement matlab contrast processing, the method used is the gray-scale tensile)

hough变换检测椭圆的matlab程序(hough transform matlab ellipse detection procedure)

说明：  《visual C++ 开发经验技术宝典》图像锐化处理，主要是将图像的亮度增加，颜色的鲜艳感增强。("Visual C++ development experience Technical Collection," Image sharpening, mainly to increase the brightness of the image, bright sense of color enhancement. )

小波包分解，对信号能够高频，低频分解，能够得到不同频带范围的信号(zi dong shezhi yu zhi)

说明：  自己编写的基于直方图均衡化以及边缘增强的程序！(I have written based on histogram equalization and edge enhancement procedure!)