DP-TBD

批量读取tiff影像并计算月份NDVI最大值(use matlab to read tiff and calculate NDVI)

guided_filter滤波器。对于图像处理方面有用。(filter. It is useful for image processing.)

用harris算法对图像进行角点检测,输出为这些点的坐标的矩阵(Harris algorithm using corner detection image, the output for these points, the coordinates of the matrix)

说明：  matlab图像滤波去噪分析及其应用 双线性滤波、Kirsch滤波、超限邻域滤波、逆滤波、双边滤波、同态滤波、小波滤波、六抽头滤波、约束最小平方滤波、非线性复扩散滤波、Lee滤波、Gabor滤波，、Wiener滤波、Kuwahara滤波、Beltrami流滤波、Lucy?Richardson滤波、NoLocalMeans滤波等研究内容。(Analysis and application of MATLAB image filtering and denoising Bilinear filtering, Kirsch filtering, transfinite neighborhood filtering, inverse filtering, bilateral filtering, homomorphic filtering, wavelet filtering, six tap filtering, constrained least square filtering, nonlinear complex diffusion filtering, Lee filtering, Gabor filtering, Wiener filtering, Kuwahara filtering, Beltrami flow filtering, Lucy Richardson filtering, nolocalmeans filtering, etc.)

说明：  EM算法是最为经典的统计图像分割方法，代码基于matlab，使用EM算法实现图像分割,源代码加实例图像，非常好用(By using EM algrithm to realize the segementation of image)

IDL中用于读取ENVI格式的遥感卫星影像数据的IDL程序(IDL, ENVI format for reading remote sensing image data on the IDL)

Implementations of image processing algorithms(This project can be built by Visual Studio 2012. Project description file included. Currently it is for Windows only.)

这个算法包实现了从多幅拥有共同结构的图像里提取出共同形状的骨架。( This package implements the algorithm for Sketching the Common as described in: Shai Bagon, Or Brostovsky, Meirav Galun and Michal Irani "Detecting and Sketching the Common", CVPR 2010)

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)

本程序的作用，完成了一种图像的DDE算法，数字图像细节增强。(The role of this procedure, the completion of an image of the DDE algorithm, digital image detail enhancement.)