superresolution_v_2[1].0

利用surf1函数绘制函数图形的三维光照图形，其中图形函数表达式为peaks(Use surf1 function to draw three-dimensional graphics of functional graphics, in which the graphical function expression is peaks)

说明：  关于GLSL的点光源，载入模型，纹理。。。。。。。。。。(light engine code OpenGL.)

实现图像颜色识别分块实现图像颜色识别分块(Block of image color recognition)

说明：  针对一维最大类间方差算法(1-Otsu)抗噪性能较低、二维最大类间方差算法(2-Otsu)计算复杂度高、实时性差的问题,提出一种基于改进的萤火虫算法(FA)优化二维Otsu图像分割算法(FA-2-Otsu).(In order to solve the problems of low anti noise performance of 1-otsu, high computational complexity and poor real-time performance of 2-otsu, an improved firefly algorithm (FA) is proposed to optimize the 2-otsu image segmentation algorithm (fa-2-otsu).)

图像SURF特征提取，用于图像拼接和篡改检测，注释比较详细，程序很好理解(SURF feature extraction, image is used for image matching and tamper detection, comments are detailed, the program is easy to understand)

形态学pathopenings的实现，对线特征提取等图像处理有令人惊奇的效果。(Morphological pathopenings the realization of the line feature extraction and other image processing have surprising effects.)

在图像传输时，常常需要对图像进行压缩，这些代码用来对图像进行压缩编码。(In the image transmission, it is often the need for image compression, the code used to image compression.)

CPML在地震波模拟中的实现源码。主要包括了二维和三维交错网格有限差分及其完全匹配层实现。(Group progam for Seismic wave modeling.CPML implemention in finite difference of seismic wave modeling. )

基于ENVI和IDL的以HDF形式存储的已角度配准过后的遥感图像的BRDF计算，使用的核为li_transit和ross_thick。同时还有IDL基础教程和ENVI用户手册。(ENVI and IDL in the order based on HDF have been stored in the form of point of registration after the calculation of remote sensing images of the BRDF, the use of nuclear as li_transit and ross_thick. Along with IDL and ENVI user manual Essentials.)

混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。(Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of)