APGL

利用粒子群算法做的关于基于指数熵对数熵TSALLIS熵的多阈值的图像分割，内附无优化算法，可以做时间比较(Using particle swarm optimization to do on the entropy-based index of the number of entropy TSALLIS entropy of multi-threshold image segmentation, containing no optimization, can do more time)

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.)

将avi视频图像转换成一帧帧的图像，并将帧图像保存到下级目录中。(Avi video images will be converted into an image frame, and frame images saved to the lower directory.)

数学建模，动态模糊与模糊图像处理，维纳滤波方法。(Mathematical modeling, dynamic fuzzy and fuzzy image processing, Wiener filtering method.)

VC++图像平滑处理+程序源码，实现图象的平滑（去噪声），锐化功能，测试时请在C盘下放一个测试用的Bmp图片，路径：C: est.bmp，没有的话没法测试。如上图所示是水平平滑处理，可以对比下，不过感觉颜色失真了，有兴趣的看一下。命令行编译过程如下： 　　vcvars32 　　rc bmp.rc 　　cl smooth.c bmp.res user32.lib gdi32.lib(VC++ image smoothing+ program source code, to achieve a smooth image (to noise), sharpening function in the C drive when testing a test of decentralization Bmp picture, path: C: test.bmp, no words can not test . Is shown above the level of smoothing, you can compare the next, but the feeling of color distortion, interested look. Command-line compiler as follows: vcvars32 rc bmp.rc cl smooth.c bmp.res user32.lib gdi32.lib)

说明：  《数字图像处理与机器视觉:Visual C++与Matlab实现》5 图像分割,霍夫变换,Hough变换直线检测的Matlab实现(&quot Digital image processing and machine vision: Visual C++ and Matlab to achieve&quot 5 image segmentation, Hough transform, Hough transform line detection of the Matlab implementation)

可以将256色度级的图像转化为灰度图象，挺值得新手学习一下的，希望大家能喜欢(256 chroma level images can be converted to grayscale images, very worth novice to learn about, and I hope you like)

focuss算法 关于图像稀疏性分解中字典学习的经典算法 和大家一同分享 (focuss algorithm on the sparse image decomposition in the dictionary of the classical learning algorithm and we share)

彩色图像的威纳滤波去噪算法，很简单的，很全，自己毕业论文的程序(Color image of the Wiener filter denoising algorithm, very simple, very full, their thesis program)

说明：  利用卷积神经网络进行特征提取，并将卷积特征可视化(Feature extraction using convolutional neural networks and visualization of convolution features)