DarionALToolbox

FDK算法 重建三维图像，期中有GPU并行计算加速fdk算法的实例(back-projection to RECONSTRUCT 3D IMAGE)

计算BBO晶体一类匹配时随非共线角变化的相位匹配角(Calculating the phase matching angle of BBO crystal with non-collinear angle)

winform 纹理效果显示图像 例子源码

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：  图像滤波，中值滤波，自适应，对比结果，图像去燥使用(Image filtering, median filtering, adaptive, comparative results, medical image use)

传统的超像素分割方法，亲测能用。可以进行后续的使用(The traditional super pixel segmentation method can be used for affinity measurement.)

数字图像处理与机器视觉(光盘),共13章源代码(Digital image processing and machine vision code)

用大整数乘法来计算两个n位二进制数相乘(With large integer multiplication to calculate the multiplication of two n-bit binary number)

基于Curvelet变换的样本块图像修复算法提高现有样本块修复算法性能。首先利用Curvelet 变换估计待修复图像的4方向特征．然后利用颜色信息与方向信息共同衡量样本块间的相似度，在此基础上构造颜色-方向结构稀疏度函数．(Based on the sample block transform image restoration algorithm Curvelet enhance existing sample block repair algorithm performance. Firstly Curvelet transform estimated to be repaired 4 directional characteristics of the image. Then use the color information and direction information together measure the similarity between the sample block, constructed on the basis of color- direction sparsity structure function.)

提取图形的七阶不变矩，简单易行，利用求得特征矩进行图像的识别(It is simple and easy to extract the seven order moment of the graph, and the image is identified by using the characteristic moment.)