动态背景

RMSHE算法（recursive mean-separate histogram equalization）。基本原理是将图像根据平均灰度进行分割，各部分分别进行直方图均衡。文件中包含RMSHE的matlab源代码，论文，以及输入图像(RMSHE algorithm (recursive mean-separate histogram equalization). The basic principle is that the image based on the average gray-scale is divided, each part respectively histogram equalization. File contains RMSHE matlab source code, papers, as well as the input image)

在最不重要位替换嵌入算法的基础上引入矩阵编码，增强水印隐蔽性，有效地提高水印嵌入效率。(On the basis of the least significant bit embedded algorithm is introduced to Replace the matrix coding, to enhance hidden watermark, the watermark embedding effectively improve efficiency.)

读入光流估计的结果，将运动矢量转化成颜色，生成颜色的光流估计图像。(Read optical flow estimation result, the color motion vector conversion, optical flow estimation to generate a color image.)

用matlab做的基于DCT变换的图像压缩源码，先把图像分成8X8 的子块，然后做DCT变换，再量化，在恢复重建图像(Using matlab to do DCT-based transform image compression source code, first image is divided into 8x8 sub-block, and then make DCT transform, and then quantified, at the resumption of the reconstructed image)

一些压缩感知的代码举例, 还请多多指教，谢谢啦(The examples of compressed sensing.)

一个计算多边形面积和周长的程序, 同时可以把多边形显示出来, 直观展现多边形形状.(A polygon area and perimeter calculation procedures, the polygons can be displayed simultaneously, intuitive show polygonal shape.)

图像重构实现干涉条纹缺陷图的缺陷检测，能很好的滤除条纹，得到缺陷(Interference fringe image reconstruction to achieve defect detection defect map can be a very good filter stripes)

本程序可以实现bmp图像的放大和缩小，希望对大家有所帮助(Bmp image of the program can zoom in and out, we want to help)

用matalab编译的winner滤波法对图像进行去噪处理，利用了FFT的方法(Compiled using matalab winner filtering method to deal with the noise of image, using the FFT method )

PIVlab是一个开源的粒子图像测速（PIV）软件。它不仅能够通过图像对计算出速度，而且能够得到、显示和导出多个参数的流动模式。通过一个友好的图形用户界面（GUI），使得的PIV技术分析和数据后处理更加快速、高效。(PIVlab is an open-source particle image velocimetry (PIV) software. It can not only calculate the speed through the image pairs, but also can get, display and export the flow patterns of multiple parameters. Through a friendly graphical user interface (GUI), the PIV technology analysis and data post-processing are faster and more efficient.)