image-convert

包含基于三种不同方法的应用于图像处理的矩阵低秩分解的matlab算法，可以将目标图像分解为一个低秩图像和一个稀疏图像之和；并包含一种基于区域生长方法的图像区域识别程序，可以用来提取图像中的目标。(Containing used in image processing based on three different methods of low-rank matrix decomposition algorithm matlab, the target image can be decomposed into a low rank of the image and the image and a sparse and contains a region growing method based on image area recognition program It can be used to extract the object in the image.)

说明：  实现简单的梯度算法，这是一个函数文件。调用函数的主程序文件以及实验用的lena图都一并上传了，使用简单，快捷。注：该程序可以直接处理彩色图像。(Implementation of simple gradient algorithm, this is a function file. The main program file of calling function and Lena diagram for experiment are uploaded together. It is easy and fast to use. Note: This program can process color image directly.)

说明：  关于全变分的图像修复算法。。 。。 。。 。。 (On the whole variational image restoration algorithm. . . . . . . .)

说明：  该代码的主要功能是图像边缘检测，利用霍夫变换确定圆的位置，并进行缺陷定位着色等操作(The main function of the code is image edge detection, using HOF transform to determine the location of the circle, and the location of defects such as coloring operations)

该程序主要实现图像的无损压缩，具体算法包括： （1）整数小波变换＋SPIHT无损压缩 其中整数小波可以采用JPEG2000提供的5－3小波，也可以采用S＋P变换，本程序均已给出。 （2）DPCM＋算术编码 DPCM采用的是JPEG中采用的方法； （3）JPEG－LS标准 JPEG－LS的无损压缩性能非常好，超过JPEG2000。(The program mainly realize lossless image compression, the specific algorithm include: (1) integer wavelet transform SPIHT Integer Wavelet lossless compression which can be used to provide the 5-3 wavelet JPEG2000, can also adopt the S P transform, the procedures are given. (2) DPCM arithmetic coding DPCM is used in JPEG methodology (3) JPEG-LS standard JPEG-LS lossless compression of very good performance, more than JPEG2000.)

基于图象小波变换的图象编码的经典算法．程序中包括压缩和解压缩的过程，有可执行的示例．(Image-Based Wavelet Transform Image Coding Algorithm classic. Procedures, including compression and decompression process, the sample has executable.)

画图源程序-图像数据显示，可以对数据进行时间轴的曲线显示，也可以是空间轴的多维显示 (Paint source- the image data, the data can be curve shows the time axis, it can be multi-dimensional display space axis)

基于平稳小波变换的图像融合。低频分量取平均，高频分量取绝对值最大。最后做逆平稳小波变换。(stationary wavelet transfrom.Averaged low frequency components, high frequency components of the maximum absolute value. Finally, do the inverse stationary wavelet transform.)

给予matlab的图像分割例程，内附相关图片及程序。(Image segmentation routine MATLAB, containing the relevant pictures and program. )

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)