Digital-Image-Processing-PPT

非精确ALM解决PCA算法的例子，用后效果不错，发上来分享。(This matlab code implements the inexact augmented Lagrange multiplier method for Robust PCA.)

维纳滤波，图像恢复，通过用matllab编辑的程序实现以上功能-wena (Wiener filtering, image restoration, with matllab editing program to achieve the above functions-wena)

关于图像处理的程序，他能实现对裂缝进行自动的提取(About image processing program, he can achieve the automatic extraction of cracks)

采用最大后验（MAP）算法来实现图像的超分辨率算法(The super-resolution algorithm using the maximum a posteriori (MAP) algorithm to achieve image)

对局部放电进行小波变换模式识别，解决放电破坏设备问题(Wavelet Transform Pattern Recognition for Partial Discharge)

利用多用方法进行数字图像去模糊实例，便于初学者理解反卷积去模糊、伪逆去模糊等基本算法(Example scripts for image debluring, try to give general introduction of some basic algorithms, such as pseudo inverse debluring.)

这是一个matlab写的输出图像直方图的程序！能让你充分理直方图的原理！(this is a matlab produce of image histogram! you can undertand the the histogram algorithm easy! )

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)

图像修复是图像处理的一个重要问题。这是一个困难的问题，同时填写在地区缺少图像信息的纹理和结构。以损坏的图像inpaint失踪的结构和纹理信息，基于小波分解的图像修补算法提出。首先损坏的图像分解成子图像和纹理结构，利用小波子图像转变。然后，地区结构中缺少的信息的子图像重建曲率驱动扩散（CDD），算法，而在同一区域的纹理子图像填写基于改进的纹理合成的典范 最后，恢复的图像，通过重组，结构和纹理恢复的结果。大量的实验表明，该算法可以快速，高效地恢复在同一时间的结构和纹理信息，视觉效果和峰值信噪比比（PSNR）比同类算法更好。(Image inpainting is an important problem image processing. It is a difficult problem to simultaneously fill-in the texture and structure in regions of missing image information. In order to inpaint the damaged image with both missing the structure and texture information, an image inpainting algorithm based on wavelet decomposition is presented. First the damaged image is decomposed into structure sub-image and texture sub-image using the wavelet transformation. Then, the sub-image with the region of missing information in the structure is reconstructed by Curvature-Driven Diffusions (CDD) algorithm, while the same region in the texture sub-image is filled-in with the improved texture synthesis based on exemplar Finally, the restored image is given by recombining the structure and texture restored results. A large number of experiments show that the proposed algorithm can quickly and efficiently restore the structure and texture information at the same time, and the visual eff)

Simple Shape Detection using Hough Transform 用霍夫变换做为形状检测的matlab源码,从matlab官网下载(Simple Shape Detection using Hough Transform Hough transform used as a shape detection matlab source, from the official website download matlab)