book

说明：  求lena图像的信息熵，图像大小为256X256(For lena image information entropy, image size is 256x256)

能够实现视频处理，利用MATLAB实现的VIEB算法，简单有效(To achieve video processing, using MATLAB implementation of the VIEB algorithm, simple and effective)

softcast的2d dct实现，先对图像转灰度图，得到的亮度信号矩阵做DCT 变换(softcast of 2d dct implementation, the first image to grayscale, luminance signal obtained by DCT transformation matrix do)

基于马尔科夫随机场的，例子学习超分辨率复原代码。(This is an implementation of the example-based super-resolution algorithm. Although the applications of MSFs have now extended beyond example-based super resolution and texture synthesis, it is still of great value to revisit this problem, especially to share the source code and examplar images with the research community. We hope that this software package can help to understand Markov random fields for low-level vision, and to create benchmark for super-resolution algorithms.)

此算法实现了分形图形分形树的一种叠代算法的实现过程。它充分利用了叠代的思想(algorithm achieved a fractal graphics fractal tree an iterative algorithm implementation process. It makes full use of iterative thinking)

说明：  车牌识别，图像分割出车牌，然后进行具体识别，matlab源代码(vehicle license plate recognition)

这是双边滤波去噪的原始代码，挺好，可以下载( de-noising by Bilateral filtering,it is very good!)

说明：  图形图像处理经典模型—tv模型的matlab源代码以及所需的图片(Graphic image processing classical model-tv model matlab source code, as well as the picture)

立体视觉代码。主要过程包括：harris角点检测，zncc最优策略匹配，扩散得到更多特征对应，计算disparity和对应颜色值。另外提供了两幅图片以及对应的特征匹配的结果和disparity图像。(Three-dimensional visual codes. The main process includes: harris corner detection, zncc optimal strategy for matching, the proliferation of more features of the corresponding calculation of disparity and the corresponding color value. Also provides a picture and two corresponding feature matching results and disparity images.)

超分辨率复原（分块处理） 将样本库中的高分辨率图象和低分辨率图象分别做分块处理，当新输入一幅低分辨率图象时，分成小块到样本库中寻找最匹配的高分辨率块，然后复原出高分辨率图象。(Superresolution recovery (block processing) for a sample of high-resolution images and low-resolution images respectively block , when the new importation of a low-resolution image, divided into small samples to find the most matching high-resolution block, then recovery from high-resolution images.)