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图像420转换为422或444的算法。高斯加噪算法，脉冲加噪算法。(YCbCr 4:2:0 image is converted to 4:2:2 or 4:4:4 of the algorithm, Matlab program is)

SVM 用于基于块划分特征提取的图像分类源码(SVM for feature extraction block-based image classification into source)

这是模式识别中线性分类器中最常用的fisher线性分类器，并在结果中画出了分类线和分类效果(This is image restoration in the Iterative Blind Deconvolution algorithm matlab source for the study of digital image has a lot of benefits!)

pca-sift算法的实现，采用vc2008开发环境，进行开发，独立运行(pca-sift algorithm, using vc2008 development environment to develop, operate independently)

Information of transform watershed

一种基于半监督的svm的图像分类方法。该方法通过聚类核的方法利用无标记样本局部正则化训练核的表达式。这种方法通过图像直接学习一个自适应的核。该程序仿真的是文章：Semi-supervised Remote Sensing Image Classification with Cluster Kernels。大家可以参考下。(A semi-supervised SVM is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image, and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictionsds)

Zernike矩，分为一个主函数和六个子函数，主要步骤有注释，方便理解，有图，方便使用。主程序前段更改了判断输入参数个数的代码(Zernike moments, divided into a main function and six subroutines, major steps have comments, easy to understand, map, easy to use. The main front-end change the code determine the number of input parameters)

基于CDD模型的快速数字图像修复算法 matlab版 (CDD model based on fast digital image restoration algorithm matlab version)

自适应直方图均衡算法，因为是从从局部入手，所以图像增强效果显著。 (Adaptive histogram equalization algorithm, because from the start from the local image enhancement effect is remarkable.)

说明：  图像融合指标，综合熵、空间频率、互信息等等，一共7种。在图像处理后的质量评价方面很有用(Fusion index, comprehensive entropy, spatial frequency, mutual information, etc., a total of 7 species. In the image processing is useful for quality assessment)