wm_noise

分享一个很好的图像处理MATLAB，代码最小全变差图像重建(Sharing a good image processing MATLAB, the code minimum total variation image reconstruction)

meanshift 分割算法meanShift,均值漂移，在聚类、图像平滑、分割、跟踪等方面有着广泛的应用。meanShift这个概念最早是由Fukunage在1975年提出的，其最初的含义正如其名：偏移的均值向量；但随着理论的发展，meanShift的含义已经发生了很多变化。如今，我们说的meanShift算法，一般是指一个迭代的步骤，即先算出当前点的偏移均值，然后以此为新的起始点，继续移动，直到满足一定的结束条件。(meanshift Segmentation algorithm)

这是一个实现了发表在CVPR2006的抠图算法。(This package contains an implementation of the matting algorithm described in the CVPR2006 paper.)

基于halcon图像处理平台的相机标定算法，相当不错。。。。。(Halcon based image processing system camera calibration algorithm is quite good. . . )

采用Graph Cut算法进行multiple-label的图像分割，算法很完整，非常适合初学者以及开发人员参考(multiple-label image segmentation using Graph Cut based algorithm, suitable for beginners and developers)

说明：  关于电子稳像的，挺有用！是用matlab为开发环境的~~(On electronic image stabilization, quite use! Is the environment for the development of matlab ~ ~)

本书是计算机视觉编程的权威实践指南,依赖 Python 语言讲解了基础理论与算法,并通过 大量示例细致分析了对象识别、基于内容的图像搜索、光学字符识别、光流法、跟踪、三维重建、 立体成像、增强现实、姿态估计、全景创建、图像分割、降噪、图像分组等技术。(This book is an authoritative guide to computer vision programming, which explains basic theories and algorithms based on the Python language.A large number of examples carefully analyze object recognition, content based image search, optical character recognition, optical flow, tracking, 3D reconstruction,Stereoscopic imaging, augmented reality, attitude estimation, panoramic creation, image segmentation, noise reduction, image grouping and other techniques.)

在linux环境下，实用c++编写的有关实用mean shift进行视频跟踪，视频分割的很好的程序，运行良好……(Practical mean shift in the linux environment, practical C++ written in the video track, video segmentation procedures, good running ...)

说明：  基于邻域的比例算法求两幅图像的差异图，用于变化检测。(The proportion of neighborhood-based algorithms to find the difference of the two images map for change detection.)

本例中主要使用四个区模糊函数对图像去模糊，分别是deconvwnr、deconvreg、deconvlucy、deconvblind。这四个函数分别适合不同的情况，对模糊图像的恢复效果也不同(In this case the main use of the four areas on the image to the fuzzy ambiguity function, respectively deconvwnr, deconvreg, deconvlucy, deconvblind. These four functions are appropriate for different situations, the recovery of the blurred image effect is different)