图像重建算法当中的FDK、ART和TV算法

gmkl算法源码，广义多核学习，可用图像多类分类。(gmkl algorithm source, the a generalized multicore learning, free image multi-class classification.)

This is simple calculator program. I m making a GUI calculator interface in MFC. this program works pretty well.

压缩感知中利用增广拉格朗日方程解最小稀疏正则化的恢复算法(DAL solves the dual problem of (1) via the augmented Lagrangian method (see Bertsekas 82). It uses the analytic expression (and its derivatives) of the following soft-thresholding operation， which can be computed for L1 and grouped L1 (and many other) sparsity inducing regularizers. If you are interested in our algorithm please find more details in our technical report or in my talk at Optimization for Machine Learning Workshop (NIPS 2009).)

全变分去噪程序，用于图像去噪，该方法能克服图像去噪中的震荡效应，使图像更加光滑，并能保存边缘信息。(total variation denoisy)

分别使用灰度值变换和频域滤波的方法实现图像的伪彩色处理。在Matlab中编程实现(The use of gray value, respectively, transform and frequency domain filtering method of pseudo-color image processing. Programming in Matlab in)

M-s中的CV方法( CV)

彩色图像转为灰度图像，灰度图像的傅里叶低通滤波，彩色图像的傅里叶低通滤波，三种滤波器的三维表示(Transforming a color image to a grayscale image Do low-pass filtering to a grayscale or color image by Fourier transform The three-dimensional representation of three types of filters )

采用基于LSB的随机图像隐藏技术实现将一幅图像（目标图像）隐藏到另一幅图像（载体图像）中。包含目标图像yctx.bmp，载体图像Miss.bmp，图像隐藏代码randlsbimghide.m文件，图像读取代码randlsbimgget.m文件，随机位选择代码randinterval.m文件。(LSB-based technique of random images to achieve a hidden image (object image) to another piece of hidden images (vector image) in the. Includes target image yctx.bmp, vector image Miss.bmp, image hidden code randlsbimghide.m file, image file reading code randlsbimgget.m, random bits select the code randinterval.m file.)

自己写的幅值谱分析的matlab程序，可以应用与机械振动信号分析中。(this is a matlab program that can analylize the amplifier of a wave.)

常规的中值滤波器，在噪声的密度不是很大的情况下效果不错。但是当概率出现的概率较高时，常规的中值滤波处理后，仍然具有噪声点，并丢失了细节和边缘，效果不是很好。 自适应中值滤波器可以尽可能的保护图像中细节信息，避免图像边缘的细化或者粗化。(The conventional median filter works well when the noise density is not very large. However, when the probability of probability is high, the conventional median filter still has noise points, and the details and edges are missing. The result is not very good. The adaptive median filter can protect details in the image as far as possible and avoid thinning or coarsening of the image edges.)