新建文件夹

NEDI源代码，是用于超分辨率重建的，并附上原文献，希望对大家有用~(The NEDI source code for super-resolution reconstruction, together with Uehara literature, we want to ~)

图像处理基本操作MATLAB原代码，包括各种经典算法，处理过程源码(Basic operation of image processing the original MATLAB code, including a variety of classic algorithms, process source)

用方向可调小波变换实现图像边缘提取，包涵实验报告及原图像及处理结果(Steerable Wavelet Transform using edge extraction, bear with lab reports and the original image and processing results)

对各种格式图像实现打开保存旋转的操作的示例工程的说明文档。(images of various formats to achieve preservation of open rotation examples of the operation of the project documentation.)

C# 以任意角度旋转图片实例

用模糊C均值算法实现图像分割 ，文件夹中附带有图片和matlab的源程序，已经验证过可以实现对图片的分割 (Fuzzy C-means algorithm for image segmentation, there is a folder with pictures and matlab source, has already been verified can be achieved on image segmentation)

java语言开发 二维码的生成与解析

迭代CT重建血管，希望对大家能够有所帮助(Iterative CT reconstruction of blood vessels, we want to be able to help)

将源图像做高斯卷积，以光滑化源图像，用于图像的预处理(source image makes convolution with gauss so as to smooth the image as a preprocess.)

：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提 出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、 叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰 度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性 评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量 机分类器进行训练(plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi)