Producing-Letters-on-7Segment

基于主成分分析与灰度（颜色）、纹理（灰度共生矩阵）的图像分类matlab程序（附示例图像）。(Image classification matlab program based on principal component analysis and gray (color), texture (GLCM) (with sample images).)

基于MATLAB的SOM神经网络设计源程序(MATLAB-based SOM neural network design source)

包含了信息融合领域近几年有影响力的多篇国内外文献(Contains many articles of influential domestic and international literaturethe in the field of information fusion in recent years, )

说明：  计算图像峰值信号比，比较边缘检测图像的质量(Calculate the image peak signal ratio and compare the quality of edge detection)

说明：  可以实现基于matlab的加权平均方法的图像融合，亲测有效。(The weighted average method based on matlab can be used for image fusion)

实现BMP文件的基本功能。用C++实现一个Class，该Class具有读写BMP文件功能；该Class还具有对图像像素进行操作的功能（针对每个像素的读、写） (BMP files to achieve the basic functions. Using C++ implementation of a Class, the Class has read and write BMP file functions the Class also features the image pixel operation function (for each pixel of the read, write))

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SL0算法，对256*256的lena图处理，比较原图和SL0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 SL0 algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using SL0 algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)

非常完整的帧差法多目标跟踪Matlab代码，并提供了完整的文档介绍，非常适合初学者学习。注：运行时要改一下文件路径，以及把视频文件转成图像序列输入。(Very complete frame difference method, multi-target tracking Matlab code, and provides a complete documentation, very suitable for beginners to learn. Note: at run time, you change the file path, and the video file is converted to an image sequence)

高斯有色噪声，有色噪声的谱估计及其在雷达中的应用(gauss noise)

说明：  SSR、MSRCR、Retinex和暗通道四种去雾算法的matlab实现，亲测好用，改图片名可以直接运行，效果很好(The matlab implementation of SSR, msrcr, Retinex and dark channel defogging algorithms is easy to test and use. Changing the image name can run directly, and the effect is very good)