COV2K

适合初学小波的同志，小波图像增强原理底层。严格高低频分解重构，没有使用小波工具库。(Suitable for beginners wavelet comrades, the underlying principle of wavelet image enhancement. Strict high-low-frequency decomposition of reconstruction, did not use the wavelet tool library.)

基于差值的可逆水印算法源码，及详解说明。还包括一篇英文论文和该作者的一些理解和解释，希望对大家有帮助(Reversible watermarking algorithm based on difference of source code, and the Detailed Description. Also includes an English papers and some of the author' s understanding and interpretation, we hope to help)

细菌觅食算法与粒子群优化算法相结合的程序，可以实现适应度函数的最值计算，有很好的参考价值。(Bacterial Foraging algorithm and particle swarm optimization combined program, you can achieve the best fitness function value, and have a good reference value.)

说明：  基于灰度矩的亚像素边缘检测，零件边缘提取(Subpixel edge contour extraction)

对双目视觉图像对进行匹配获取视差图，为后续三维重建做基础。(Binocular vision images are matched to obtain the disparity map, as a foundation for the subsequent three-dimensional reconstruction.)

光学计算中跟衍射相关的很多公式，有详细说明，很好用(err)

本实验要求编写一个计算压缩-解压缩图像的均方根误差、均方信噪比的程序。该程序是一个通用程序。编写程序产生图示的结果。使用上面的保真度准则程序计算可视信息的损失特性。(Prepared in this experiment, a calculation of compression- decompression of the root-mean-square error of image, mean square signal to noise ratio of the procedure. The procedure is a common procedure. Picture shows the preparation of the results of procedures. The use of the above criteria for the fidelity of calculated loss characteristics of visual information.)

双目立体匹配，使用SAD，SSD，NCC算法，比较多种常见算法的优劣，对其优化实现快速准确匹配。(Binocular stereo matching, the use of SAD, SSD, NCC algorithm, compare the merits of a variety of common algorithms, and its optimization to achieve fast and accurate matching)

说明：  本设计为基于MATLAB的指纹识别系统。本设计系统主要对指纹图像进行三方面处理：图像预处理、特征提取和特征匹配。图像预处理包括四个步骤：图像灰度化、滤波增强、二值化、细化，对指纹图像进行预处理后，去除了原图像的冗余部分，方便后续的识别处理；特征提取主要是提取指纹图像细化后的端点和分叉点；特征匹配是利用两个指纹的图像进行特征点比较，来确定两幅图像是否来自于同一手指。(This design is a fingerprint identification system based on MATLAB. This system mainly deals with fingerprint image in three aspects: image preprocessing, feature extraction and feature matching. Image preprocessing includes four steps: image grayscale, filter enhancement, binarization and thinning. After preprocessing the fingerprint image, the redundant part of the original image is removed for the convenience of subsequent recognition processing. Feature extraction is mainly to extract the endpoint and bifurcation points after thinning the fingerprint image. Feature matching is to determine the two images by comparing the feature points of the two fingerprint images Whether the image comes from the same finger.)

采用改进的CV图像分割算法用于提取图像边缘轮廓，代码注释很详细。(CV improved image segmentation algorithm for extracting image edge profile, the code is very detailed notes.)