diskreet.m

说明：  用Visual C++实现模糊图像恢复处理程序实例(Using Visual C++ fuzzy image restoration processing examples)

使用snake方法进行图像处理的原代码，简单准确(use snake image processing methods for the original code simpler and more accurate)

程序实现Matlab下爬山法(hill-climbing)方法，用于彩色图像分割。(mage is first converted to CIE lab color space and the 3d color histogram is generated. Hill-climbing algorithm is used to find the local maximums in lab histogram, which is used to automatically decide the cluster number K as well as initial seeds for )

说明：  matlab生成的fig图纸转换为DXF文件(Converting fig drawings generated by MATLAB into DXF files)

在统计学中，皮尔逊积矩相关系数（英语：Pearson product-moment correlation coefficient，又称作 PPMCC或PCCs[1], 文章中常用r或Pearson s r表示）用于度量两个变量X和Y之间的相关（线性相关），其值介于-1与1之间。在自然科学领域中，该系数广泛用于度量两个变量之间的相关程度。它是由卡尔·皮尔逊从弗朗西斯·高尔顿在19世纪80年代提出的一个相似却又稍有不同的想法演变而来的。这个相关系数也称作“皮尔森相关系数r”。(In statistics, Pearson product-moment correlation coefficient (English: Pearson product-moment correlation coefficient, also known PPMCC or PCCs [1], the article used or Pearson s r r representation) is a measure of the two variables X and Y between correlation (linear correlation), a value between-1 and 1. In the natural sciences, the coefficient is widely used to measure the degree of correlation between the two variables. It is made a similar Karl Pearson Francis Galton in the 1880s but made a slightly different idea evolved. The correlation coefficient is also called Pearson s correlation coefficient r .)

包涵了计算影像的几种质量指标，这些指标都是全局指标，对影像质量评价有一定作用(Image Quality Index ：IFC、Q4、QNR、VIF)

根据图像四个方向的共发矩阵，求取四个灰度共发矩阵的熵，求熵的均值生成特征矩阵，通过聚类分析实现图像的纹理分割。(In accordance with the direction of a total of four images fat matrix, to strike a total of four gray hair matrix entropy, and entropy generation characteristics of the mean matrix, through cluster analysis of the realization of image texture segmentation.)

编程实现了抑制sar图像相干斑噪声的精致lee滤波算法，效果很好(refined lee)

核聚类算法KFCM源代码，对传统FCM算法的改进(Nuclear clustering algorithm KFCM source code, the traditional FCM Algorithm)

photoshop sample font