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基于SIFT的图像配准程序 SIFT特征匹配算法是目前国内外特征点匹配研究领域的热点与难点,其匹配能力较强,可以处理两幅图像之间发生平移、旋转、仿射变换情况下的匹配问题,甚至在某种程度上对任意角度拍摄的图像也具备较为稳定的特征匹配能力(Hot and difficult of the current research areas of domestic and foreign feature point matching based the SIFT image registration program SIFT feature matching algorithm is its ability to match between the two images, can handle translation, rotation, affine transformation case match problem, even in some degree images taken at any angle with more stable feature matching ability)

说明：  对于双基地SAR，基于IDL语言使用BP算法进行成像，以及显示回波信号。(For Bistatic SAR, BP algorithm is used to image and display echo signal based on IDL language.)

最小二乘法拟合圆vc实现，最小二乘法(least squares analysis)是一种 数学 优化 技术，它通过 最小化 误差 的平方和找到一组数据的最佳 函数 匹配。 最小二乘法是用最简的方法求得一些绝对不可知的真值，而令误差平方之和为最小。(least squares fitting round vc realized, least-squares method (least squares analysis) is a mathematical optimization techniques, It error by minimizing the square and a group of data to find the best matching function. Least-squares method is the most simple method to derive absolutely true value unknown, and so the sum of squared errors to the minimum.)

高光谱遥感图像光谱解混的独立成分分析技术(Hyperspectral remote sensing image spectra unmixing independent component analysis)

国外网站上找到的一个图像拼接程序，将三张图片拼接为一张。 大致方法为Haaris找角点，粗匹配，RANSAC精匹配。 有详细的注释，附带一篇代码说明文档，非常适合初学者学习研究(Foreign web sites to find a image stitching procedure, three picture mosaic as one. Roughly method for Haaris to find a corner, coarse matching, the RANSAC precise matching. Detailed notes, with a code documentation, very suitable for beginners to learn)

毕业论文参考，内含代码，基于小波变换的图像去噪算法(Image denoising based on wavelet transform, the graduation design code, matlab code)

图像修复的一些常用模型，与tv模型，cdd模型，bscb模型，还有基于纹理的修复方法。(Some common model image restoration, and tv model, cdd model, bscb model, as well as repair method based on texture.)

说明：  比SSIM更新的图像质量评判算法VSNR(image quality assessment VSNR and its matlab code)

基于最小二乘法的相位解缠VC++算法 (least squares method based on the VC Phase Unwrapping Algorithms)

detect face and recognition with lpd and pca