lvbo

使用cnn提取图像特征，然后用SVM分类，此处没有给出训练集，另外imagenet-caffe-alex部分代码需要注意，需要下载的话把注释掉的代码打开(Using CNN image feature extraction, and then use the SVM classification, there were no given training set, also need to pay attention to imagenet-caffe-alex part of the code, you need to download the commented code open)

tutorial压缩感知代码。压缩感知，又称压缩采样，压缩传感。它作为一个新的采样理论，它通过开发信号的稀疏特性，在远小于Nyquist 采样率的条件下，用随机采样获取信号的离散样本，然后通过非线性重建算法完美的重建信号(The tutorial compression-aware code. Compressed sensing, also known as compressed sampling, compressed sensing. It as a new sampling theory, through the development of signal sparsity, in conditions far less than the Nyquist sampling rate, using random sampling for discrete samples of the signal, and then through the non-linear reconstruction algorithm perfect reconstructed signal)

将RF信号处理为B型图像的工具包,非常好用(a toolkit to transfer RF signal to B-mode images)

基于matlab的JPEG编解码程序。里面有2个程序，一个是程序是编解码过程，一个纯编码过程。含解释。(Program based on Matlab JPEG codec. There are two programs, one program is the process of encoding and decoding, a pure coding process. Containing explained.)

快速的Lee滤波算法，代码经过优化，修正和测试，能有效减少SAR图像中的斑点噪声。(Fast Lee filter algorithm, code optimized, modification and testing, can effectively reduce the SAR image speckle noise.)

说明：  基于Kmeans算法的图像分割，一般Kmeans是数据挖掘中用来聚类的，本试验利用图像中的灰度值作为Kmeans算法的原始点，进行图像分割(Kmeans algorithm based on image segmentation, data mining in general Kmeans is used to clustering, the trial use of the image gray value as the original algorithm Kmeans spots for Image Segmentation)

说明：  基础SAD算法代码，最基本的图像匹配处理方法，也是最常用的基本法，有助于新手理解(Basic sad algorithm code)

全变分去噪 J=tv(I,iter,dt,ep,lam,I0) 输入: I - 灰度图像, iter - 迭代次数 [1], dt - 时间步长 [0.2], ep - 提升参数epsilon [1], lam - 保真项 lambda [0], I0 - 输入噪声图像 [I0=I] ([]中的是缺省值)( 全变分去噪 J=tv(I,iter,dt,ep,lam,I0) 输入: I - 灰度图像, iter- 迭代次数 [1], dt - 时间步长 [0.2], ep - 提升参数epsilon [1], lam - 保真项 lambda [0], I0 - 输入噪声图像 [I0=I] ([]中的是缺省值))

BP神经网络，可以处理图像进行分类等各种处理的源码(BP neural network image processing source code)

实现图像识别，基于小波变换实现图像特征提取，然后神经网络进行训练(The image recognition is realized, the image feature extraction is realized based on wavelet transform, and then the neural network is trained.)