circular-birght-target-detect

说明：  数字图像处理中非常有用的一些图片包括LENA等.(Digital image processing is very useful in a number of pictures, including LENA, BARBARA such.)

利用pcnn和nsct实现图像融合，非下采样Contourlet变换，脉冲耦合神经网络，连接强度(Using NSCT and PCNN image fusion, the sampling Contourlet transform, the pulse coupled neural network, the connection strength)

介绍Opencv这个图像处理库环境下的GMM建模与EM算法，从数学的角度深入分析高斯建模和EM算法(Introduce the the Opencv this image processing GMM modeling the EM algorithm in the library environment, in-depth analysis from a mathematical point of the Gaussian model and the EM algorithm)

基于VisualC_和DCMTK的医学DICOM图像显示与调窗,同时在MFC 下完成鼠标拖动进行窗宽/窗位的调节(Medical image of DICOM VisualC_ and DCMTK display and window based on, at the same time in MFC complete the mouse to drag the window width/window regulator )

说明：  实现了图像与视频的信息隐藏，输入图片和视频可进行效果演示(The information hiding of image and video is realized)

黑塞矩阵（Hessian Matrix），又译作海森矩阵、海瑟矩阵、海塞矩阵等，是一个多元函数的二阶偏导数构成的方阵，描述了函数的局部曲率。黑塞矩阵最早于19世纪由德国数学家Ludwig Otto Hesse提出，并以其名字命名。黑塞矩阵常用于牛顿法解决优化问题，利用黑塞矩阵可判定多元函数的极值问题。在工程实际问题的优化设计中，所列的目标函数往往很复杂，为了使问题简化，常常将目标函数在某点邻域展开成泰勒多项式来逼近原函数，此时函数在某点泰勒展开式的矩阵形式中会涉及到黑塞矩阵。(Hessian Matrix, is a square matrix of the second order partial derivative of the multivariate function, which describes the local curvature of the function. The Hesse matrix was first proposed by the German mathematician Ludwig Otto Hesse in the 19th century and named after it. Hesse matrix is often used in Newton method to solve the optimization problem, using the black plug matrix to determine the extreme function of the extreme value problem.)

一份retinex算法的code，其中利用pca 做 色度分解，利用改进的bilateral滤波器进行低频和高频分解(Retinex algorithm in a code, which made use of PCA colority decomposition, the use of improved bilateral filter for low frequency and high-frequency decomposition)

说明：  快速磁共振成像领域的GRAPPA（generalized auto-calibrating partially parallel acquisitions）算法实现(GRAPPA realization in fast MRI)

压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ILRS算法，对256*256的lena图处理，比较原图和IRLS算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间(Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and IRLS algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using IRLS algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)

全参考型视频质量评价方法，基于机器学习和视频特征提取，发表与2012年TMM(FR VQA method based on machine learning and video feature )