wavelet-image-fusion

根据D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis 以及 C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision.这两篇文献提供的方法编写的图像分割代码，作者是 Chris M. Christoudias, Bogdan Georgescu，代码经我看了后加了丰富的中文注释，希望可以给各位带来阅读上的方便。 基于meanshift的聚类分割方法包括滤波、区域融合等操作，通过调整sigma和sigmar来调整分割效果。(According to D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis, and C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision. These two documents prepared by the methods provided by image segmentation code , the author is Chris M. Christoudias, Bogdan Georgescu, after I read the code, add a rich Chinese notes, hoping to bring you the convenience of reading. Segmentation method based on clustering meanshift including filtering, regional integration and other operations, and by adjusting the sigma sigmar to adjust segmentation results.)

说明：  After spending a decade cooking in Michelin-starred restaurants in Spain, chef Oswaldo Oliva returns to his native Mexico City with a menu that rivals that of Pujol

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)

冲激函数、阶跃函数及三角函数的小波变换、模极大线并求它们的李氏指数(The impulse function, step function and wavelet transform of the trigonometric function, the modulus maxima line and the Lipschitz exponents)

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. )

输入两幅大小等已配准相同的图像，计算两幅图像的结构相似度，可以应用于图像各种处理，基于人类视觉，是一个很好的图像相融合质量评价效果(Enter two sizes, etc., have the same image registration, two images to calculate structural similarity, may be applied to a variety of image processing based on human vision, it is a very good image quality uation of the effect of integration)

最完整的CCA典型相关分析程序，分析两幅图像，相似度，匹配，融合都可以使用，分析天气变量也可以使用。(The most complete CCA canonical correlation analysis program, analysis of two images, similarity, matching, fusion can be used, analysis of weather variables can also be used.)

说明：  基于opencv的图像灰度化，将24位彩色图像重新量化为8位灰度图像(gray image)

说明：  调用 envi5.3自带的FMask云检测功能，并输出掩膜文件(Call the FMash cloud detection function of envi5.3 and output the mask file)

说明：  本程序是用来做图像编码的，主要采用的算术编码的思想(This procedure is used for image coding, the main idea used in arithmetic coding)