matlab_watermark

说明：  在光学系统中根据光学系统的孔径函数生成MTF曲线(n the optical system, the MTF curve is generated according to the aperture function of the optical system)

说明：  盲迭代反卷积算法，可以在不求PS的情况下恢复图像(Iterative blind deconvolution algorithm, can not seek restoration of the PS images)

说明：  将基于小波变换的水印嵌入后的宿主图像加噪处理再提取灰度水印。(will be based on the wavelet transform the watermark embedded in the host Image Noise from Gray again watermark.)

屏幕差异传输优化例程，程序结合易语言扩展界面支持库和数据操作支持库，调用API函数实现屏幕图像传送的差异优化。(Screen difference transmission optimization routines, programs combining easy language support library expansion interface and data manipulation support library , call the API function to differentiate screen image transfer optimization.)

基于压缩感知的图像恢复重建的实验源代码以及相关图片及文件(Compressed sensing image reconstruction experiment source code and related images and documents)

自己写的双阈值分割算法，可以用在多个场合(Write their own dual-threshold segmentation algorithm can be used in a number of occasions)

从xsl文件中读入数据并计算其wigner_ville分布，内含详细说明。(Xsl file reads from the data and calculates its wigner_ville distribution, containing detailed instructions.)

说明：  图像分割，还可以，kmeans聚类的方法。k-means算法是一种聚类算法，所谓聚类，即根据相似性原则，将具有较高相似度的数据对象划分至同一类簇，将具有较高相异度的数据对象划分至不同类簇。(K-means algorithm is a clustering algorithm. The so-called clustering is to divide data objects with high similarity into the same class cluster and data objects with high dissimilarity into different class clusters according to the similarity principle.)

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)

This a very useful program from a book. It is mainly used for face-detecting.It also includes interface-program.