GVF

经典的斑块分析算法，计算Blob的源码，算法很好。(Classical plaque analysis algorithms to calculate Blob source code, algorithms very well.)

基于粒子群算法的（pso）的otsu图像分割算法。(Based on Particle Swarm (pso) image segmentation algorithm of Otsu.)

图像修复是图像复原研究中的一个重要内容, 它的目的是根据图像现有的信息来自动恢复丢失的信息，其可以用于旧照片中丢失信息的恢复、视频文字去除以及视频错误隐藏等。(Image restoration image restoration is an important part of the study, its purpose is to present the information according to the image to automatically recover lost information, which can be used for missing information in the old photo restoration, video, text and video error concealment removal .)

本程序使用dcmtk库，可以显示大部分的DICOM图像。非压缩、压缩黑白图像以766灰阶显示，彩色图像正常显示。 对于初用dcmtk库的开发者来说，入门较难，本程序已使用VC6工程将主要库加入工程中，可以参考本工程的设置。另外，对于高于8位的黑白图像，本程序也提供一个处理方法的参考。(This procedure used dcmtk library, you can see that the majority of the DICOM image. Non-compressed, compressed to 766 gray-scale black-and-white images show that color image display. Dcmtk library used for the early developers, the entry more difficult, the procedure has been in use for the main library VC6 project will join the project, can refer to the project settings. In addition, higher than the 8-bit black-and-white images, the program also provides a reference method.)

用来对2维信号的频域进行sinc插值处理，以放大图像。插值的结果较为理想(Used for 2-D frequency domain signal processing to sinc interpolation in order to enlarge the image. Interpolation more satisfactory results)

改进的otsu算法，计算局部otsu的值确定局部小区域。对于光照不均匀图像处理有比较好的适应( Improved otsu algorithm to calculate the value of the local otsu determine the local small area. For uneven illumination image processing has a relatively good adaptation)

数字图像处理之分水岭图像分割法matlab实现源码(Digital image processing of watershed image segmentation method to achieve source matlab)

图像二值化，去离散噪声，彩色图像灰度化，特征提取，灰度化，彩色图像平均灰度(Image binarization, to discrete noise, color gray image feature extraction, gray, color image mean gray)

该源代码主要是利用bagging,adboosting等集成学习的方法进行图像融合处理，效果甚好！(The main source is the use of bagging, adboosting ensemble learning methods such as image fusion, the effect is very good!)

全参考型视频质量评价方法，基于机器学习和视频特征提取，发表与2012年TMM(FR VQA method based on machine learning and video feature )