ImgToRaw

说明：  图形处理功能，有滤波功能，边缘提取等各个方面，值得一看(Graphics processing capabilities, has filtering, edge detection and other aspects, see)

VC++版的傅里叶变换函数代码，已经调试过了(program for F code)

说明：  基于边缘特征的SAR图像与光学图像匹配，测度为Hausdorff距离(SAR images based on edge features and optical image matching, for the Hausdorff distance measure )

vc++ 数字图象处理 很好用我看了以后收益非浅希望大家喜欢(vc digital image processing with very good after I read greatly benefit hope you like)

图像拼接小程序，使用直接对比的方法进行拼接并用加权平均的方法进行融合 (a simple program for image splicing )

模式识别经典方法之一 K均值算法（KMeans）进行图像分割 有完善的图形界面 可以通过柱状图的形式动态展示聚类程序(One of the classical method of pattern recognition K means algorithm (KMeans) for image segmentation has improved graphical interface can be dynamically displayed in the form of histogram clustering procedure)

实现图像的锐化浮雕等效果。当然，这是我自己弄了很多次之后的。我也忘了它实现的是什么效果了，反正就是大概图片处理一类的。(To achieve the effect of image sharpening and other relief. Of course, I get many times. I also forget to realize what effect, anyway, is about image processing class.)

这是一个比较完整的KPCA算法，通过这个算法可以快速的找到主要的特征向量进行主成分分析。(This is a relatively complete KPCA algorithm, this algorithm can quickly find the main feature vector principal component analysis.)

1、参数初始化； 2、形成边界削波数据； 3、波场初始化； 4、Zmax层波场延拓到深度Zmax-1； 5、Zi+1层波场延拓到深度Zi； 6、重复5，从Iz Nz-1开始，直到Iz 1，得测线上的频率—空间域波场； 7、频率-空间域波场对频率做反傅里叶变换，得时间-空间波场； (1, parameter initialization 2, the formation of boundary clipping data 3, wave field initialization 4, Zmax layer wave field extended to the depth of Zmax-1 5, Zi+1 layer wave field extended to the depth of Zi 6, repeat 5, starting Iz Nz-1 until Iz 1, the frequency of the test line- spatial domain wave field 7, frequency space domain wave field to frequency inverse Fourier transform, time- space field )

一幅大小为256×256的256级灰度图像（每像素8bit） 如果图像的各个像素之间是统计独立的，求该图像的信息熵 -离散无记忆信源 如果图像的各个像素之间不是统计独立的，求该图像的信息熵 -图像像素的概率分布模型(A 256 gray scale image (8bit per pixel) with a size of 256 x 256 If each pixel of the image is statistically independent, the information entropy of the image is calculated Discrete memoryless source If each pixel of the image is not statistically independent, the information entropy of the image is calculated Probability distribution model of image pixels)