YUV2Image

利用极大似然估计进行盲反卷积复原，通过多帧图像约束，效果较好(Use of maximum likelihood estimates for blind deconvolution restoration by the multi-frame image constraint, the better)

说明：  《数字图像处理与机器视觉:Visual C++与Matlab实现》5 图像分割,霍夫变换,Hough变换直线检测的Matlab实现(&quot Digital image processing and machine vision: Visual C++ and Matlab to achieve&quot 5 image segmentation, Hough transform, Hough transform line detection of the Matlab implementation)

基于纹理及光谱信息融合的遥感图像分类方法研究，硕士论文，有关纹理和遥感图像分类很好的学习素材(Texture and spectral information based on the integration of remote sensing image classification method, master' s thesis, the classification of remote sensing image texture and good learning material)

说明：  基于Kmeans算法的图像分割，一般Kmeans是数据挖掘中用来聚类的，本试验利用图像中的灰度值作为Kmeans算法的原始点，进行图像分割(Kmeans algorithm based on image segmentation, data mining in general Kmeans is used to clustering, the trial use of the image gray value as the original algorithm Kmeans spots for Image Segmentation)

用于显著性检测的对比算法SP的MATLAB版本，比较新的是2013年的，亲测可用(MatLAB version of SP, a contrast algorithm for saliency detection, is relatively new in 2013, and pro-test is available.)

说明：  用于实现图像超分辨率重建的不同算法的matlab程序，可直接编译(Used to achieve super-resolution image reconstruction algorithm matlab program can be compiled directly)

实现图像配准和计算配准均方根误差的功能，人工选取控制点，包括四种转换方法。(Achieve image registration and registration RMSE calculation function, manual selection control points, including four kinds of conversion methods.)

对比五种模糊图像复原算法，包括：维纳滤波算法、约束最小二乘滤波算法、Richardson-Lucy（RL）算法、循环边界算法和最优窗算法。(introduces the five kinds of image restoration algorithms, including wiener filtration, constrained least squares filtering, Richardson-Lucy, cyclic boundary algorithm and optimal window algorithm.)

简易二值化演示易语言源码，例程程序使用嵌套循环改变画板颜色演示了图像二值化的简单过程。(Simple binary demonstrate easy language source code , a routine procedure uses nested loop Sketchpad color change demonstrates the binary image of a simple process .)

去模糊的算法，有MIT做的，参考文献是siggraph06的文章，效果很好(Go fuzzy algorithms, MIT has to do the references are siggraph06 the article, very good)