LBP

形态学图像增强算法，包括对比度增强和边缘锐化(Morphological image enhancement algorithms, including contrast enhancement and edge sharpening)

灰度差分统计法提取图像纹理特征，包括平均值，对比度，熵，等特征，matlab函数，直接接使用，无需修改，中文注释，通俗易懂(Gray difference statistical method is used to extract texture features of image, including mean value, contrast, entropy, etc., matlab function, directly used without modification, Chinese annotation, and easy to understand)

图象数据透明化源码，主要使用两种方法使图象透明显示：一种是用画板载入画图片，画出方式选择透明白色；另一种是将图象数据字节集化，并按高度和宽度逐一改变颜色使颜色透明。(Transparent image data source, the main use of the image transparent display two methods : one is to use Sketchpad loaded Picture , Draw Mode Select transparent white; the other is a set of bytes of image data , and press)

实现信号的离散傅立叶变换和反变换以及滤波，平滑处理(Realize signal discrete Fourier transform and inverse transform, as well as filtering, smoothing treatment)

首先读入图像，转化成hsi彩色空间，然后只对亮度I分量作直方图均衡化，最后合成三个分量返回到RGB图，但是这种方法容易使颜色失真，失去了部分图像细节。(First read in the image, into the hsi color space, and then only on the brightness I component for histogram equalization, the final synthesis of the three components of return to RGB chart, but this method is easy to make color distortion, loss of part of image detail. )

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. )

说明：  多基线SAR，InSAR相位解缠代码，Matlab版本，对应论文：Robust Two-Dimensional Phase Unwrapping for Multibaseline SAR Interferograms: A Two-Stage Programming Approach ，含数据、代码及验证结果(Multi baseline SAR, InSAR phase unwrapping code, matlab version, corresponding paper: robot two dimensional phase unwrapping for multibaseline SAR interferograms: a two stage programming approach, including data, code and verification results)

CVPR2013中出现的一种显著性特征检测算法，该算法用于模拟人眼视觉识别过程，提取图像中的显著性特征借后期目标对象的检测跟踪等使用，该算法(Context-Aware Saliency Detection。matlab implementation of an classical saliency detection alrogithm,which imitate our recognition process of eyes)

使用马尔可夫随机场进行图像分割，包括标记场、特征场建模和参数估计等，程序可运行。(image segmentation Using Markov random field , including label Field, features field modeling and parameter estimation, the program can run.)

全变分自适应去噪模型张红英matlab平台，(Full variable adaptive denoising model Zhang Hongying matlab)