m_files

说明：  一个简单的画图程序，提供基本图元点、线、面，图元填充、裁剪的操作(Simple Draw)

vc编写数字图像处理的程序,其中有图像处理的各种方法的程序(Vc write a digital image processing program, including the various methods of image processing program)

很好用的肤色分割模型，在YCBCR颜色空间内分割，经过开运算和闭运算，取得很好的结果(Good use of color segmentation model, YCBCR color space segmentation, after opening operation and closing operation, and achieved good results)

说明：  使用log-Gabor滤波器对图像进行滤波程序。(The use of log-Gabor filter image filtering process.)

提取糖尿病视网膜病变眼底图像上硬性渗出。将彩色图像转为灰度图，对灰度图进行形态学处理、去背景、阈值分割，最终将原图上的硬性渗出区域用绿色标记出来。(this word is to extrac the hard exudates on diabetic retinopathy fundus image. The color image to grayscale on grayscale morphological processing to the background, threshold segmentation, ultimately on the original hard exudates area marked in green.)

说明：  基于python-opencv的图像匹配代码，包含了SIFT、SURF、AKAZE、ORB方法。43-48行修改图像路径，74-81修改匹配方法。(dev: python3.6+opencv_python==3.4.2+opencv-contrib-python==3.4.2)

实现混合高斯的功能，再次基础上可进行任意操作(The function of mixed Gaussian can be realized, and any operation can be carried out on the basis of it again.)

像素与图像基本指标—彩色图像的基本信息、像素颜色值、图像的大小、矩阵形式等、对灰度图像计算有关统计参数：（1）图像的大小；（2）图像的灰度平均值；（3）图像的自协方差；（4）图像的灰度标准差、对图像进行45度，19度，98度等任意角度的旋转，并计算原图与旋转后图像的相关系数和协方差矩阵(Pixel and Basic Index of Image)

说明：  包括pca和lda两个文件，先用pca降维后进行lda进行分类，用于人脸识别，实验结果很好。(It includes 2 files pca and lda. After the dimensionality reduction of pca, use lda for classification in face recognition. It has a very good experimental results.)

说明：  当前论文主要考虑的是非信号依赖的高斯噪声下的图像恢复，本程序实现了泊松噪声下的图像恢复，泊松噪声为信号依赖噪声，能够更加有效逼近实际成像系统噪声。( This is the code that was used in the papers "A Nonnnegatively Constrained Convex Programming Method for Image Reconstruction", "Total Variation-Penalized Poisson Likelihood Estimation for Ill-Posed Problems", "Tikhonov Regularized Poisson Likelihood Estimation: Theoretical Justification and a Computational Method", "An Efficient Computational Method for Total Variation with Poisson Negative-Log Likelihood", "An Analysis of Regularization by Diffusion for Ill-Posed Poisson Likelihood Estimation," "An Iterative Method for Edge-Preserving MAP Estimation when Data-Noise is Poisson", and finally, "Regularization Parameter Selection Methods for Ill-Posed Poisson Maximum Likelihood Estimation". See my publications page for more details. The main algorithm is for nonnegatively constrained, regularized Poisson likelihood estimation. At this point you can choose Tikhonov, total variation regularization, and diffusion regularization. A number of other methods are also implemented. Regularizatio)