Image_Rotation

运用于人脸图像预处理中，主要实现功能有：人脸检测，人脸对齐，人脸图像放缩，人脸剪切。(Applied to the face image preprocessing, the main features are: face detection, face alignment, face image zoom, face cuts.)

说明：  图像处理中应用非常广泛的二维最小均方误差算法（TDLMS）。绝对好用！(Image Processing is widely used two-dimensional MMSE algorithm (TDLMS). Absolute ease of use!)

本书详细介绍了医学图像注册方面的知识，包括配准的原理、刚性配准、非刚性配准，对于医学图像处理方面的研究具有很重要的参考价值。(The book details the registration of medical image information, including the principle of registration, rigid registration, non-rigid registration for medical image processing research has important reference value.)

一个师兄编写的图像配准程序，其中sift代码匹配以及错误点检测值得参考学习。(A brother write image registration procedures, which sift code matches the reference point of error detection is worth learning.)

一种简单高效地基于压缩感知的跟踪算法。首先利用符合压缩感知RIP条件的随机感知矩对多尺度图像特征进行降维，然后在降维后的特征上采用简单的朴素贝叶斯分类器进行分类。该跟踪算法非常简单，但是实验结果很鲁棒，速度大概能到达40帧/秒(A simple and efficient tracking algorithm based on compressed sensing. Firstly, with the random sensing matrix compressed sensing RIP conditions for multi-scale image feature dimension reduction, and then use the naive Bias classifier simple classification in the feature reduction after the. The tracking algorithm is very simple, but the results are robust, speed can reach 40 frames per second)

利用hough变换检测图像中的形状圆和直线，以及图像几何变换(detecting image circle or line using hough transformation and transformation)

本程序实现位图的读写，均衡化，滤波，LBP，LTP特征提取以及基于LBP，LTP和卡方距离，最近邻分类的人脸识别，批量给图片改名等功能(The program can read and write a bitmap, equalization, filtering, LBP, LTP feature extraction and nearest neighbor classifier for face recognition based on LBP, LTP and chi-square distance, rename picture and some other functions.)

基于多帧图像插值（Interpolation）技术的方法是SR恢复技术当中最直观 的方法。这类方法首先估计各帧图像之间的相对运动信息，获得HR图像在非均 匀间距采样点上的象素值，接着通过非均匀插值得到HR栅格上的象素值，最后 采用图像恢复技术来去除模糊和降低噪声（运动估计!非均匀插值!去模糊和 噪声）。(In this paper, we propose a novel method for solv- ing single-image super-resolution problems. Given a low-resolution image as input, we recover its high- resolution counterpart using a set of training exam- ples. While this formulation resembles other learning- based methods for super-resolution, our method has been inspired by recent manifold learning methods, par- ticularly locally linear embedding (LLE). Speci?cally, small image patches in the low- and high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector correspond- ing to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness con- straints between patches in the target high-resolution image through overlapping. Experiments show that our method is very ?exible )

求取相机响应函数，并绘制相对曝光量-灰度曲线(Camera response function, and draw the relative exposure- gray curve )

2014年西安电子科技大学数字图像处理上机实验，同时是研究生和本科生的代码及作业 (2014 Xi' an University of Electronic Science and Technology of digital image processing experiments on board, while the graduate and undergraduate students and job code)