oawginteraction2

该源代码主要是利用bagging,adboosting等集成学习的方法进行图像融合处理，效果甚好！(The main source is the use of bagging, adboosting ensemble learning methods such as image fusion, the effect is very good!)

基于MAP的超分辨率重建算法，附有说明，使得大家方便运行。(The super-resolution by map.)

一种效果比较好的高光谱解混算法，模拟数据以及真实数据的实验都在，直接可以跑。(An effect good hyperspectral unmixing algorithm, experimental simulation data and real data are in, you can directly run.)

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.)

数字图像处理诸多算法源代码，图像处理的同学可以下载下来看看，适合初学者。(c++ code of digital image process )

各向异性扩散相干斑降噪，可以在增强边缘的同时滤出噪声，效果很好！(Anisotropic diffusion, coherent noise can enhance the edge of the filter out the noise at the same time, the effect is very good!)

简单的灰度图像的K均值聚类分割，Matlab实现(gray image segmentation using K-means clustering by matlab.)

graphcut 图像分割经典论文 英文版(graphcut image segmentation classic paper in English)

基于多尺度MRF模型的图像分割算法。使用提升小波对原始图像进行分解，使用ICM算法进行分割处理(Multi-scale MRF model based image segmentation algorithm. The original image using lifting wavelet decomposition, the use of ICM segmentation algorithm)

首先生成如下形式的图片：图中的白条为7象素宽，210象素高，白条间的宽度为17象素。 应用多种滤波器进行处理：算术均值，几何均值，谐波均值，逆谐波均值，中值滤波，中点滤波，最大值、最小值滤波等。注意，需改变模板大小。详细比较上述的结果，并分析原因。 (First generation following form images: figure IOUs 7 pixels wide, 210 pixels high, the width is 17 pixels between IOUs. Using a variety of filters for processing: the arithmetic mean, geometric mean, harmonic mean, inverse harmonic mean, median filtering, filtering midpoint, maximum, minimum filtering. Note that the need to change the template size. A detailed comparison of these results and analyze the reasons.)