project

光线跟踪，计算机图形学的实验，实现的是消隐，光线之间的遮挡等(Ray-tracing, computer graphics experiments, realize that the hidden light, such as between the block)

说明：  一个pcnn方面的程序，比较原始，但是效果好(A PCNN procedures, more original, but effective)

说明：  此算法使用于单波段或者多波段图像中的非监督异常检测，性能很好~(The algorithm used in single-band or multi-band image unsupervised anomaly detection, performance very good ~)

基于轮廓来寻找流动部分，效果不是很好，但是还可以拿来用(Based on the outline to find the flow part, the effect is not very good, but also can be used with)

这是一种彩色图像去雾算法，可以识别物体的轮廓，但是同时也会加强这些轮廓，虽然去雾效果不错，但是会加强轮廓！(This is a color image to fog algorithm can identify the contours of the object, but will also enhance the outlines, although to fog effect is good, but will strengthen the profile!)

压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SL0算法，对256*256的lena图处理，比较原图和SL0算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 (Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and SL0 algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using SL0 algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)

图形图像感兴趣区域提取的方法，个人总结的~可以直接运行！(Graphic method of region of interest extraction, can be directly run personal summary!)

fy3（hdf5格式）mersi数据投影到等经纬度(projecting fy3 mersi hdf5 format to longlat )

黑塞矩阵（Hessian Matrix），又译作海森矩阵、海瑟矩阵、海塞矩阵等，是一个多元函数的二阶偏导数构成的方阵，描述了函数的局部曲率。黑塞矩阵最早于19世纪由德国数学家Ludwig Otto Hesse提出，并以其名字命名。黑塞矩阵常用于牛顿法解决优化问题，利用黑塞矩阵可判定多元函数的极值问题。在工程实际问题的优化设计中，所列的目标函数往往很复杂，为了使问题简化，常常将目标函数在某点邻域展开成泰勒多项式来逼近原函数，此时函数在某点泰勒展开式的矩阵形式中会涉及到黑塞矩阵。(Hessian Matrix, is a square matrix of the second order partial derivative of the multivariate function, which describes the local curvature of the function. The Hesse matrix was first proposed by the German mathematician Ludwig Otto Hesse in the 19th century and named after it. Hesse matrix is often used in Newton method to solve the optimization problem, using the black plug matrix to determine the extreme function of the extreme value problem.)

说明：  利用python写的简单降噪程序（根据一个点A的RGB值，与周围的8个点的RBG值比较，设定一个值N（0