基于引导滤波的图像增强算法

凸多边形检测和显示--先把图像中的显著区域提取出来，然后用最小凸多边形囊括显著区域，并把凸多边形显示出来(Convex polygon detection and show- first images extracted significant area, and then use the smallest convex polygon covering a significant area, and the convex polygon displayed)

说明：  图像融合中的平均梯度、相关系数、信息熵、交叉熵、联合熵、均方误差、互信息、信噪比、峰值信噪比、均方根误差、空间频率、标准差、均值、扭曲程度、偏差指数等等(Average gradient, correlation coefficient, information entropy, cross-entropy, joint entropy, mean square error, mutual information, signal-to-noise ratio, peak signal-to-noise ratio, root mean square error, spatial frequency, standard deviation, mean, distortion degree, deviation index, etc. in image fusion)

用于灰度图像二值化的MATLAB代码，算法采用迭代法(For gray-scale image binarization MATLAB code, algorithm uses iterative method)

这是用来做直方图匹配的工具，测试过可以实现(Histogram Matching Tool)

说明：  用matlab编写的图像复原源代码，采用维纳滤波复原技术，可对模糊和加噪的图像实现精确复原。(Prepared using matlab source code image restoration using Wiener filter restoration technology, can be vague and processors realize accurate image noise resilience.)

由点云数据（point cloud）生成网格（mesh）的matlab实现(Converts a pointcloud (unordered vertices) to a mesh of triangles by connecting the nearest 3 points and calculates the normal of each vertex and triangle. Also finds the neighboring triangles of each vertex and the neighboring triangles of each triangle. )

基于DCT的jsteg图像隐写及分析算法，MATLAB实现。 JSteg的算法主要思想是将秘密消息嵌入在量化后的DCT系数的最低比特位上，但对原始值为O、1的DCT系数不进行嵌入。提取秘密消息时，只需将载密图像中不等于0、l的量化DcT系数的LSB取出即可。(The main idea of the JSteg algorithm is to embed secret messages in the lowest bit of the quantized DCT coefficients, but the DCT coefficients of the original values are O and 1 are not embedded. When extracting secret messages, only the LSB of the quantized DCT coefficients not equal to 0 and l in the encrypted image can be taken out.)

The code contains the method to extract minutiae and also to enhance the image

数字图像处理的非常面全的图片，包含有各种格式的和大小的。(there are many kids of images that used to digitial image pricessing,including lena,boat,cman images)

波束空间DOA的估计,从图中可以很好的看出角度的估计还是可以的。(Beam-space DOA estimation, from the figure we can see the point of a good estimate is still possible.)