GoDec

通过Markov随机场（MRF）实现图像的分割，例程给出了图像分割为3类的MRF算法(By Markov random field (MRF) for image segmentation, image segmentation routine gives the MRF algorithm for the three classes)

建立了普通话语音性别数据库,提出联合梅尔频率频谱系数(Mel2f requency Cep st rum Coefficient s , MFCC) 的特征提取方法和支持向量机(Support Vector Machine , SVM) 的分类方法进行说话人性别识别,并与其它分类方法进行比较。(A Chinese speech ( mandarin ) database was established for speaker s gender recognition. A combination met hod is p roposed for gender recognition of speaker s based on support vector machine and Mel2f requency cep st rum coefficient s (MFCC) for classification and feat ure ext raction respectively.)

基于学习单幅图像超分辨率重建算法源代码。(based on the singal picture super resolution founding)

根据CVPR最佳论文实现的基于暗通道的图像去雾算法（含soft matting），以及算法改进：包括对色彩失真的改进以及一种达到soft matting效果的快速算法(According CVPR Best Paper realized based on dark channel images to fog algorithms (including soft matting), and algorithm improvements: Improvements include color distortion as well as a way to achieve fast algorithm for soft matting effect)

说明：  去卷积 图像复原 去卷积操作 matlab(deconvolution Image restoration)

说明：  PWLS算法主要用于CT重建前的投影数据处理，能够很好的用于CT迭代算法中，对图像进行去噪(PWLS algorithm is mainly used for projection data processing before CT reconstruction. It can be well used in CT iterative algorithm to denoise images.)

在做图像处理时用遇到图像的阈值分割，这是几种图像的阈值分割方法(In doing image processing with experience image threshold segmentation, which is one of several image thresholding segmentation method)

arnold图像置换，可以用于图像加密。(arnold image replacement, can be used for image encryption.)

将RGB格式的图片进行mosaic处理得到RawData格式的图片，然后再恢复成RGB格式彩色图片,laroche-CFA插值算法(turn an RGB picture to RawData picture，then recover the RawData picture to an RGB picture.it is called laroche-CFA.)

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)