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采用基于RL(Richardson-Lucy)算法的超分辨图像复原。能够实现图像的频谱外推，得到期望的复原图像。(The RL (Richardson-Lucy) algorithm-based super-resolution image restoration. Extrapolation of image spectrum, to obtain the desired restored image.)

代码为matlab源程序，主要功能是对干涉SAR数据进行相位解缠。(Matlab source code, the main function is to phase unwrapping interferometric SAR data.)

检测鼠标信息，C++Builder精选编程学习源码，很好的参考资料。(Detecting mouse information, C++Builder select learning programming source code, a good reference.)

简单的几个四叉树分解程序，主要用于图像的分割(Several simple quadtree decomposition procedure, mainly used for image segmentation)

说明：  利用模糊神经网络的方法实现遥感图像的目标识别(The use of fuzzy neural network approach to achieve the goal of remote sensing images to identify)

这是我们自己做的一套投影仪标定程序，反投影精度在1个象素之内(This is to do our own set of projector calibration procedures, back-projection at a pixel accuracy of)

说明：  对水下模糊的图像用偏振的物理方法实现去雾的matlab代码，附水下图片(The physical method of polarization is used to realize the matlab code of fog removal for the underwater blurred image, and the underwater image is attached.)

基于小波阈值法来实现图像去噪，这是小波阈值去噪的程序代码。(Based on wavelet thresholding denoising method to achieve this is wavelet thresholding procedure code.)

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. )

基于MSRCR和SSR算法实现一个基本的图像去雾程序(A basic image fogging program based on MSRCR and SSR algorithm)