图像增强

一个很好的马尔科夫随机场（MRF）模型应用实例。(Examples for the application of markov random field.)

基于均值法的背景建模的前景目标提取。适合刚接触这一块的人。(Background modeling method based on mean foreground object extraction.)

说明：  遥感图像的道路提取。基于数学形态学处理的道路提取(Road Extraction from Remote Sensing Images)

形态学图像增强算法，包括对比度增强和边缘锐化(Morphological image enhancement algorithms, including contrast enhancement and edge sharpening)

改进了的动态视频目标跟踪程序，跟踪效果很好，大家放心下载！(Improved tracking of dynamic video program, track good effect, and we rest assured downloads!)

实现基本的LMS算法和归一化LMS算法学习曲线的比较仿真(LMS algorithm and normalized LMS algorithm learning curve comparing simulation)

图像、视频经常因为抖动而造成模糊，去模糊也是图像处理领域的难点和热点。算法实现单方向匀速运动的图像去模糊，是一种简化处理方式。(Images, video, often due to jitter caused by vague, to blur the field of image processing is also difficult and hot. Algorithm for single images to the direction of uniform motion blur, is a simplified approach.)

用哈夫曼算法对图像进行压缩， 1.颜色模式转换及采样； 2．DCT变换（离散余弦变换）； 3．量化；4．编码（有算术编码和哈夫曼编码两种，这里采用哈夫曼编码）

图像稀疏表示源代码，实现输入图像的稀疏表示，有MOP,KSVD等算法(Matlab m-file code of Iamge Representation,it can run，contains MOP，KSVD.)

cvpr2012_oral On Multiple Foreground Cosegmentation(In this paper, we address a challenging image segmentation problem called multiple foreground cosegmentation (MFC), which concerns a realistic scenario in general Webuser photo sets where a finite number of K foregrounds of interest repeatedly occur over the entire photo set, but only an unknown subset of them is presented in each image. This contrasts the classical cosegmentation problem dealt with by most existing algorithms, which assume a much simpler but less realistic setting where the same set of foregrounds recurs in every image. We propose a novel optimization method for MFC, which makes no assumption on foreground configurations and does not suffer from the aforementioned limitation, while still leverages all the benefits of having co-occurring or (partially) recurring contents across images. Our method builds on an iterative scheme that alternates between a foreground modeling module and a region assignment module, both highly efficient and scalable. In part)