clean_algorith_target_radar

说明：  图像融合算法，exercise1:为基于小波变化与局部能量的图像融合方法 exercise2:为基于自适应算法的图像融合算法 myfunction1~myfunction5:为性能指标评价函数(Image fusion algorithm, exercise1: wavelet-based change and the local energy method of image fusion exercise2: adaptive algorithm based on image fusion algorithm myfunction1 ~ myfunction5: performance indicators for the evaluation function)

ostu图像分割阈值算法,对Ostu图像分割最优阈值进行优化处理，极大缩短了搜索图像阈值计算时间，与传统的枚举法Otsu方法相比,在计算时间上具有显著的优点。(ostu threshold image segmentation algorithm for image segmentation Ostu optimal thresholds to optimize the processing, greatly reduces the search image thresholding computation time, with the traditional enumeration method Otsu methods, in the calculation of time has significant advantages.)

基于PCA主成分分析算法和Yale人脸数据库，采用Matlab语言实现的人脸识别程序，整个程序分为预处理、训练、识别、GUI界面控制等几个模块，注释清晰，通俗易懂。(Based on PCA principal component analysis algorithm and Yale face database, the face recognition program is implemented in Matlab language. The whole program is divided into several modules, including pretreatment, training, recognition, GUI interface control and so on. The annotation is clear and easy to understand.)

Zernike矩，分为一个主函数和六个子函数，主要步骤有注释，方便理解，有图，方便使用。主程序前段更改了判断输入参数个数的代码(Zernike moments, divided into a main function and six subroutines, major steps have comments, easy to understand, map, easy to use. The main front-end change the code determine the number of input parameters)

基于MATLAB对输入图像的进行灰度化处理(Grayscale processing of the input image based on MATLAB)

Multi-Scale Structural Similarity Index (MS-SSIM). Download here. Please cite the following paper in any published work if you use this software. Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," IEEE Asilomar Conference Signals, Systems and Computers, Nov. 2003.

使用tensorflow框架搭建神经网络，从单目图像中估计深度值(Using tensorflow framework to build a neural network to estimate depth from monocular images)

斯坦福大学概率图模型课程作业，利用马尔科夫随机场进行图像分割，能运行，附有英文说明文档(Stanford University Probability Chart Model Assignment, using Markov random field for image segmentation, operation, with English documentation)

c 实现 canny算子(c realize canny )

数字图像处理实验-图像处理-含实验题目及源代码(Pseudo-Color Image Processing (a) Implement Fig. 6.23, with the characteristic that you can specify two ranges of gray-level values for the input image and your program will output an RGB image whose pixels have a specified color corresponding to one range of gray levels in the input image, and the remaining pixels in the RGB image have the same shade of gray as they had in the input image. You can limit the input colors to all the colors in Fig. 6.4(a). (b) Download the image in Fig. 1.10(4) and process it with your program so that the river appears yellow and the rest of the pixels are the same shades of gray as in the input image. It is acceptable to have isolated specs in the image that also appear yellow, but these should be kept as few as possible by proper choice of the two gray-level bands that you input into your program.)