互信息图像配准

图像混色源码例程,程序结合易语言应用接口支持库和位图操作支持库，调用API函数实现图像的混色效果。(Source image blending routines, combined with easy language application program interface to support libraries and bitmap operations support library , call the API functions for image blending effect .)

此代码为运用brovery进行像素层的融合，主要用于图像处理中的图像融合(This code is applied brovery the pixel level fusion, mainly for image processing of image fusion)

使用Visual C++编写的生成三次Hermite曲线的源代码。(Bicubic Hermite code in Visual C++.)

位图操作图形选取源码，例程程序结合易语言位图操作支持库，填充小图形的背景颜色。(Select the source bitmap graphics operations , routine program combining bitmap operations easy language support library , filled with small graphic background color.)

基于偏微分方程的遥感图像处理方法 利用测试图像对算法进行仿真验证，并与其它图像去噪算法进行去噪效果比较，通过数值对降噪效果进行评估，得出该方法不仅能够去除噪声而且能够很好的保护图像的边缘等细节信息，弥补了高阶非线性扩散去噪方法导致边界模糊的缺陷，提高了遥感图像的质量，改善了视觉效果(Simulation algorithm of remote sensing image processing methods based on partial differential equations using the test image denoising results compare with other image denoising algorithm to assess the value of the noise reduction effect, draw the method can not only remove the noise but well protected image edge details, to make up for the higher order nonlinear diffusion denoising method lead to fuzzy boundaries defects and improve the quality of remote sensing images and improve the visual effects)

说明：  在一图像中加入噪声，是超分辨率中的一个重要步骤哦(noise)

AutoCAD二次开发的图像处理代码,对彩色图像进行区域生长,然后提取边界,实现图像分割.(AutoCAD second development of image processing code, color images of regional growth, and then extract the boundary, realize image segmentation.)

完成车道线检测，且运行效率高，附带图片测试(it has a high performance over lane detection, and leave pictures for testing)

说明：  tiff图像的读取与处理，并且说明清新，有很大的帮助。(The reading and processing of the tiff image, and the description of the fresh, is of great help.)

1.本程序功能包含显示直方图，OTSU的单值分割，OTSU的二值分割,OTSU的三值分割 2.在OTSU的分割时，通常情况下我们是通过类间方差得到一个分割阈值，但是有时候我们需要得到各灰度级别的目标，那么采取多值分割是 很必要的，通过改进得到OTSU的多值分割方法 3.本程序另外还有显示直方图，计算程序运行时间，处理结果以新的窗口显示等细节，这部分适合MFC初学者。(1. This procedure shows the histogram feature, OTSU single segmentation, OTSU binary segmentation, OTSU three segmentation 2. OTSU segmentation, it is usually the case we are through the between-cluster variance by a partition threshold , but sometimes we need to be the gray level of the target, then the multi-segmentation is necessary, through improved access to multi-OTSU segmentation methods 3. This procedure in addition to show the histogram to calculate run time, treatment the results of a new window display the details of this part of MFC for beginners.)