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CS压缩传感的初级教学代码，使用OMP重构，已注释，包括1维信号，2维图像的重构，分别使用dct和小波稀疏，列扫描和分块法进行omp重构(CS compressed sensing primary teaching code using OMP remodeling, already commented, including a 1-dimensional signals, 2-dimensional image reconstruction, respectively, using the DCT and wavelet sparse column scanning and sub-block method omp Reconstruction)

遗传算法与BP神经网络结合，可以进行彩色图像和灰度图像的分割。(Genetic algorithm and BP neural networks that can be color images and gray-scale image segmentation.)

基于Hilbert变换的相位差测量法分析及改进 (Based on the analysis phase measurement and improvement of the Hilbert transform)

本程序是基于二维最大熵的灰度图象分割，它考虑了两方面的因素：点灰度和邻域平均灰度。()

本算法一种基于量子遗传算法的图像增强方法，包含多个m子文件。使用量子旋转门(The algorithm based on quantum genetic algorithm of image enhancement methods, contains more than one m subfolders. The use of quantum revolving door)

方向加速法（Powell）是一种直接法，不用计算目标函数的梯度，仅通过比较目标函数的数值大小来移动迭代点就可以求出极值。(Powell use for registration)

（1）计算图像中每个像素点的LBP模式(等价模式,或者旋转不变+等价模式)。 （2）然后计算每个cell的LBP特征值直方图，然后对该直方图进行归一化处理(每个cell中，对于每个bin,h[i]/=sum,sum就是一副图像中所有等价类的个数)。 （3）最后将得到的每个cell的统计直方图进行连接成为一个特征向量，也就是整幅图的LBP纹理特征向量； 然后便可利用SVM或者其他机器学习算法进行分类识别了。((1) calculate the LBP pattern of each pixel in the image (equivalent mode, or rotation invariant + equivalent mode). (2) then the LBP eigenvalue histogram of each cell is calculated, and then the histogram is normalized (for each cell, for each bin, h[i]/=sum, sum is the number of all the equivalent classes in a pair of images). (3) finally, the statistical histogram of each cell is connected into a feature vector, that is, the LBP texture feature vector of the whole picture. Then, SVM or other machine learning algorithms can be used for classification and recognition.)

基于正则化项的最大后验概率重建的一段程序，简单易懂(Based on regularization item maximum a posteriori probability reconstruction of a program, simple and understandable )

利用curvelet波将不同聚焦图像进行分解，然后利用局部能量最大的方法将图像合成，得到清晰的图像、(Curvelet waves using different focus image decomposition, and then make use of local energy will be the largest image synthesis methods have been given clear images,)

根据icip会议2005年度的一篇文章编写的matlab程序。包含有效果图。 运用smqt（连续均值量化变换）算法进行灰度图像增强。它比传统方法（直方图均衡化处理）的优点在于可以保证增强后图象的直方图波形不变，可以展现图象的底层结构，自动集中改进图象细节。是一种新的效果更好的灰度图象增强算法。(under icip 2005 annual meeting of an article prepared by the Matlab procedures. Includes map results. Smqt use (continuous quantitative mean transform) algorithm for gray-scale image enhancement. It is superior to the traditional method (histogram equalization processing) has the advantage can guarantee enhanced image histogram waveform unchanged. able to display images of the bottom structure, automatic focus on improving image details. Is a new and better results gray image enhancement algorithms.)