Lucy-Richardson

混合高斯分布EM算法，可以算混合高斯分布的三个参数。混合高斯分布更接近系数分布。(mixed Gaussian distribution EM algorithm can calculate mixed Gaussian distribution of the three parameters. Mixed closer to the Gaussian distribution coefficient.)

说明：  实现了双线性插值、三次样条插值、B样条插值和最大阶次优化差值等传统多项式图像插值办法(Traditional polynomial image interpolation methods such as bilinear interpolation, cubic spline interpolation, B spline interpolation and maximum order optimization difference are realized.)

读取一24位彩色图像，计算其以下图像特征： 1）灰度直方图 2）颜色直方图（RGB3个通道） 3）方向梯度直方图（HOG，将图像分成4*4 16个块，8个梯度方向） (Read a 24-bit color images, calculate the following image features: 1) Histogram 2) color histogram (RGB3 channels) 3) histogram of oriented gradients (HOG, the image is divided into 4* 4 16 blocks, 8 gradient directions))

实现了基于压缩传感理论的超分辨率图像重构算法(Compressed sensing theory-based super-resolution image reconstruction algorithm)

利用MATLAB自带的两幅RGB彩色图像根据对图像互相关性的分析进行图像配准。(Using two RGB color images MATLAB, image registration is performed based on the analysis of image crosstalk.)

九点标定halcon 代码，纯halcon代码未转换成其他语言(Marking machine image recognition Halcon code)

说明：  在光学系统中根据光学系统的孔径函数生成MTF曲线(n the optical system, the MTF curve is generated according to the aperture function of the optical system)

能计算图像的灰度直方图，及其对应的灰度均值(calculate means ，variance，bins)

用FCM 对自然图像 sar图像以及纹理图像进行快速分割 ，用一维和二维直方图分别对图像进行特征映射 从而快速的对图像进行分割 包含结果截图 (By FCM sar image of natural images quickly and texture image segmentation, with one-dimensional and two-dimensional histogram of the image feature maps were so fast image segmentation screenshot contains the results)

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)