Compressive-Image-Super-resolution

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)

28个vc++图像处理原程序之--对比度拉伸(28 vc++ Image processing of the original procedure- contrast stretching)

说明：  matlab下面立体匹配和光流场计算代码，希望能对大家有所帮助。(matlab the following three-dimensional matching and optical flow field calculation code, hoping to be helpful to everyone.)

实现dicom图像的窗宽窗位调节和边缘提取(Window width adjustment and edge extraction of DICOM image)

立体匹配中的单目标定,输入图片，得出结果，官方实例(Monocular calibration)

图像边缘提取，边缘整合，有效提取边缘，并进行填充。(Image edge detection, edge integration, effective edge extraction and filling.)

基于暗通道先验理论的去雾算法，使用rgb暗通道计算大气光及透射率获得去雾图像(Since the scene radiance is usually not as bright as the atmospheric light, the image after haze removal looks dim. So we increase the exposure of J(x) for display.)

该程序为直方图均衡化程序，附有GUI界面，操作简单，可现实处理前后图像对比及处理前后的直方图分布情况显示，使用方便。(The program for histogram equalization program, with GUI interface, simple operation, the histogram distribution around the reality that is processing the image before and after treatment, easy to use.)

说明：  利用那些已经被破坏的区域的边缘， 即边缘的颜色和结构，根据这些图像留下的信息去推断被破坏的信息区的信息内容，然后对破坏区进行填补 ，以达到图像修补的目的(Using the edge of the damaged area, i.e. the color and structure of the edge, the information content of the damaged information area can be inferred from the information left by these images, and then the damaged area can be filled to achieve the purpose of image repair.)

sift 图像特征提取算法源码，对研究sift算法很有帮助。(The source code of sift image feature extraction algorithm is very helpful to the research of sift algorithm.)