04-Feng

图像处理基本操作MATLAB原代码，包括各种经典算法，处理过程源码(Basic operation of image processing the original MATLAB code, including a variety of classic algorithms, process source)

论文+ppt，我的数字图像处理课程设计，题目是基于均值滤波和小波分析的图像去噪方法(Thesis+ppt, digital image processing course design, subject image denoising method based on mean filter and wavelet analysis)

本实例实现了以下功能： 　　1.39条码生成功能 　　2.EAN-13条码生成功能 　　3.Code128条码生成功能 　　4.QR Code码制的二维条码生成 　　5.二维码制的解析功能    

数字图像处理实验——直方图的均衡化，打开图像并实现其均衡化(Digital image processing experiments- histogram equalization, open the image and realize its equalization)

光场图像渲染技术，利用稀疏分层算法对图像深度进行分层渲染，再利用颜色一致性约束进行去噪处理，通过所给出的图像阵列渲染出中间各个视点的图像(Optical field image rendering technology, the use of sparse hierarchical algorithm for image depth rendering, and then the use of color consistency constraints for noise processing, through the given image array to render the image of the middle of the various views)

写成gauss-gamma双窗函数，计算含有相干斑噪声的sar图像的梯度(The gauss-gamma double window function is used to calculate the gradient of the SAR image with the speckle noise)

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)

基于小波变换的水下图像目标检测论文加matlab实现代码(Wavelet-based underwater image target detection papers plus matlab code)

Jsteg隐写算法[23][24]由D. Upham提出，该算法将秘密信息嵌入在量化后的DCT系数的LSB上，但值为-1、0、+1的DCT系数例外。提取秘密信息时，也只是将隐藏信息的图像中不等于-1、0、+1的量化DCT系数的LSB取出即可(steganography of jsteg)

基于 halcon做的单相机+机械手图像拼接(Image stitching of single camera + manipulator based on Halcon)