pre_processing

说明：  （1）图像基本操作：不同格式（大于3种）图像的读入与存盘、文字叠加、不同彩色空间的转换、图像的DCT及FFT变换等； （2）图像增强：包括直方图拉升（线性和非线性）、直方图均衡、平滑与锐化（采用不同的滤镜），美颜（加分项）； （3）图像恢复：几何操作（如旋转、缩放、投影校正等）、模糊恢复（如运动模糊消除等，加分项）； （4）图像合成（加分项）：实现换头、换背景、图像拼接等功能。((1) Basic operation of image: reading and saving of images in different formats (more than 3 kinds), text superposition, conversion of different color spaces, DCT and FFT transformation of images, etc; (2) Image enhancement: including histogram lifting (linear and non-linear), histogram equalization, smoothing and sharpening (using different filters), beauty (bonus item); (3) Image restoration: geometric operations (such as rotation, scaling, projection correction, etc.), blur restoration (such as motion blur elimination, etc., plus points); (4) Image synthesis (Bonus): realize the functions of head changing, background changing, image splicing, etc.)

基于图像纹理的特征提取，使用灰度共生矩阵方法。(Texture feature extraction based on the use of gray level co-occurrence matrix method.)

一个很实用的GVF SNAKE程序，对于新手来说，可以通过代码的学习，掌握GVF SNAKE的意义和处理步骤(A very practical GVF SNAKE procedures, for the novice, it can be through the code learning, GVF SNAKE grasp the meaning and processing steps)

对图像进行简单处理，对光斑圆拟合，求圆心与半径(Simple processing of the image, fitting the spot circle, finding the center of the circle and radius)

本件包含四种二维高通滤波器，他们分别是基于汉宁窗，布莱克曼，凯泽窗和巴特利特窗设计的二维高通滤波器。(This piece contains four two-dimensional high-pass filter, they are based on Hanning, Blackman, Kaiser window design windows and Bartlett two-dimensional high-pass filter.)

BMP,ICO,JPG等图片格式转换，按照例子可以任意格式转换(BMP, ICO, JPG and other image format conversion, according to examples can be converted to any format.)

说明：  matlab边界自动跟踪程序，该程序可以直接运行，涉及边界判别准则和搜索准则。图像要求为二值图像，可输出为边界的点的坐标和轮廓图像。(matlab boundary automatic tracking program can be run directly involving border criteria and search criteria. Image requirements for binary images can be output for the coordinates of border points and the outline of the image.)

BPFA 算法用于图像去噪 详细的程序与说明(BPFA for image denoising)

说明：  使用pca方法对图像进行特征提取，对训练集的20个人的共一百张人脸进行训练，使用adaboost算法生成强分类器，可以对测试集的人脸图片进行识别，且识别率较高(The PCA method is used to extract the features of the image, and the training is carried out for a total of 100 faces of 20 people in the training set. The AdaBoost algorithm is used to generate a strong classifier, which can recognize the face images in the test set with a high recognition rate)

说明：  Z. Liang, J. Xu, D. Zhang, Z. Cao, L. Zhang, "A Hybrid L1-L0 Layer Decomposition Model for Tone Mapping," in CVPR 2018.