RegsiterFM

这是一个去背景并且进行阈值分割的程序，阈值的选择采用迭代法(This is the background to a threshold value and segmentation procedures, the threshold value of the options using the iterative method)

利用克隆选择和粒子群算法实现图像分割 有很好的分割效果。经过多次验证实践。(Clonal selection and particle swarm algorithm has a good image segmentation segmentation. After several rounds of verification practice.)

说明：  图像超分辨率是指从一序列低分辨率观测图像中重建高分辨率图像。图像超分辨率充分利用了各帧图像 间相似而又不同的信息。(Compared with the model derived from Kolmogorov spectrum, the new model introduces more complete transcendent constraints and is more similar to the physical characteristics of atmospheric turbulence.)

各种图像分割算法程序，里面包含好几个文件夹，每一个文件夹都有程序(matlab image sengmentation)

说明：  图像分割，还可以，kmeans聚类的方法。k-means算法是一种聚类算法，所谓聚类，即根据相似性原则，将具有较高相似度的数据对象划分至同一类簇，将具有较高相异度的数据对象划分至不同类簇。(K-means algorithm is a clustering algorithm. The so-called clustering is to divide data objects with high similarity into the same class cluster and data objects with high dissimilarity into different class clusters according to the similarity principle.)

说明：  基于各种算法的运动视频背景消除。使用opencv工具库，通过读取视频文件，处理视频，最后存储。(Background Subtract Algorithm)

说明：  基于Online Learning for Matrix Factorization and Sparse Coding的在线字典学习算法，本压缩包为作者的源代码(The online dictionary learning based on the paper "Online Learning for Matrix Factorization and Sparse Coding". This compression package is the author's source code.)

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)

非局部均值滤波 输入: 待平滑的图像 t: 搜索窗口半径 f: 相似性窗口半径 h: 平滑参数 NLmeans(ima,5,2,sigma) (Non-local mean filtering input: to be a smooth image t: the search window radius f: similarity of the window radius h: smoothing parameter NLmeans (ima, 5,2, sigma) )

通过对图像加燥，运用叠加运算对加燥后的图像进行去燥处理(Removal of Gauss noise by addition operation)