CBIR

易语言图像混色汇编版源码，易语言写的图形图象编程，很好的参考。(Easy language image color mixing assembly version of source code, easy to graphics programming language to write, a good reference.)

数字水印实现数字水印实现数字水印实现数字水印实现(The digital watermark digital watermark digital watermark to achieve)

说明：  对数据的独立性进行分析，对数据的预处理（中心化和白化处理）(The independence of data analysis, data pre-processing (center-based and the whitening process))

快速的Lee滤波算法，代码经过优化，修正和测试，能有效减少SAR图像中的斑点噪声。(Fast Lee filter algorithm, code optimized, modification and testing, can effectively reduce the SAR image speckle noise.)

对图像进行处理，利用拉普拉斯锐化进行边缘检测。(The image is processed and the edge is detected by Laplasse sharpening.)

光斑中心提取算法，在VS2010下，用opencv2.3.1编写的控制台程序，主要功能是利用hession矩阵和高斯滤波进行光斑中心点提取，里面附测试图片，提取精度为亚像素级别。(Spot center extraction algorithm, in VS2010, written by opencv2.3.1 console program, the main function is to use hession matrices and Gaussian filter for extracting the center spot, which is attached test images, the extraction accuracy of sub-pixel level.)

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)

第27章 透明位图背景上的滚动字幕实例该程序可以方便实现字幕的滚动效果，程序简单易懂(transparent bitmap on the background of rolling subtitles examples of the procedures to facilitate the achievement of subtitles rolling results, procedures simplified)

说明：  医学图像重建算法中OSEM算法的Matlab程序(MATLAB Program of OSEM Algorithms)

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).)