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说明：  GI 鬼成像源代码,关联算法和一些数据读取存储等问题 的解决方案(ghost imaging code)

使用区域生长法对灰度图像进行分割，显示原图及分割后的图像(Region growing method using gray-scale image segmentation, image display artwork and divided)

说明：  模拟飞机爬升的仿真程序，可输出各种飞行姿态，很不错(Simulating an aircraft climb of the simulation program can output a variety of flight attitude, very good)

说明：  数字图像处理，图像的增强算法，源代码，希望能有所帮助。(Digital Image Processing and Image Enhancement Algorithms)

对图像进行边缘检测的程序，实例为经典的lena图像。(The image edge detection procedures, examples of classic lena image.)

说明：  图像处理算法质量评估，图像结构相似度（ssim)评价的matlab实现，并附带原始论文pdf(This is an implementation of the algorithm for calculating the Structural SIMilarity (SSIM) index between two images. Please refer to the following paper: Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error measurement to structural similarity" IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004.)

1、对观测数据进行中心化，； 2、使它的均值为0，对数据进行白化—>Z； 3、选择需要估计的分量的个数m，设置迭代次数p<-1 4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）； 5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的） 6、用对称正交法处理下W 7、归一化W(:,p)=W(:,p)/norm(W(:,p)) 8、若W不收敛，返回第5步 9、令p=p+1，若p小于等于m,返回第4步 剩下的应该都能看懂了 基本就是基于负熵最大的快速独立分量分析算法(1, on the center of the observation data, 2, making a mean of 0, the data to whitening-> Z 3, select the number of components to be estimated m, setting the number of iterations p < -1 4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers) 5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p)) 8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm)

SAR运动目标成像，包含了多个点目标，结果比较清晰(SAR Moving Target Imaging)

彩色图像边缘检测（canny 算子，高斯窗口可调）(image edge detection [canny])

基于多尺度形态学提取眼前节组织，已经调试成功，效果明显，可直接使用(Extraction of anterior segment tissue based on multi-scale morphology has been successfully debugged, and can be used directly.)