图像去雾

基于SUFT的特征提取与匹配，内涵详细的文档说明，很好的实现了特征提取与匹配，该方法较基本的SIFT有很大的提高(Extraction and matching of features based on SUFT, detailed content of the documentation, very good realization of the feature extraction and matching, SIFT the method is basic is greatly improved)

图像去模糊处理，分别运用了维纳滤波器、deconvreg方法、deconvlucy方法、盲反卷积等方法。非常实用(Image deblurring processing, namely the use of the Wiener filter, deconvreg method, deconvlucy methods, blind deconvolution methods. Very useful)

数字图像隐藏及提取研究分析论文、文献等。主要研究算法为LSB算法和DCT奇偶量化、DCT均值量化。(Digital Image Information Hiding and Extraction of analytical papers, literature. The main algorithm for the LSB algorithm and DCT parity quantization, DCT mean quantization.)

该代码是对图像进行二值化处理，进行图像分割(The code is to binarize the image, the image segmentation)

snakes are a variational technique to find the best contour,(Snakes are a variational technique to find the best contour,)

PIVlab - 时间分辨粒子图像测速（PIV）工具： 一种基于GUI的工具，用于预处理，分析，验证，后处理，可视化和模拟PIV数据。 使用MATLAB网络研讨会进行人脸识别代码： 使用MATLAB在线讲座的人脸识别中的主要演示文件。 Gabor特征提取： 该程序生成一个自定义Gabor滤波器组; 并使用它们提取图像特征。 主成分分析： 用于特征提取； 链码： 基于MATLAB的freeman的曲面轮廓描述(PIVlab - time resolved particle image velocimetry (PIV) tool: A GUI based tool for preprocessing, analysis, validation, post processing, visualization, and Simulation of PIV data. Using MATLAB webinar for face recognition code: The main demo file is used in MATLAB online lectures for face recognition. Gabor feature extraction: The program generates a custom Gabor filter bank and uses them to extract image features. Principal component analysis: For feature extraction; Chain code: Surface contour description of Freeman based on MATLAB)

自己编写的函数，自动寻找乳腺肿块区域的种子点，为种子区域生长做准备，算法比较复杂(breast boundary.seed region growth)

压缩感知，稀疏表示采用小波基表示，压缩测量采用随机高斯矩阵，重构算法是omp重构(Compressed sensing, sparse representation using wavelet representation, compression measurements using random Gaussian matrix remodeling reconstruction algorithm is omp)

matlab_基于象素的图像配准程序,很好使用,精度也很高,要用的就下载吧!(matlab_ pixel-based image registration procedures, a very good use, accuracy is also high on the download you want to use!)

K-Means算法,不要求建立模型之后对结果进行新的预测，没有相应的标签，只是根据数据的特征对数据进行聚类。主成分分析降维对数据进行可视化操作,对features进行降维.(K-Means algorithm does not require the establishment of the model after the new prediction of the results, there is no corresponding tag, but only on the characteristics of data clustering data. The principal component analysis reduces the dimension, carries on the visualization operation to the data, reduces the dimension to the features.)