bilinear_scale

：％中值滤波％otsu 自动阈值计算％利用4个方向的roberts和进行边缘提取,并二值化％膨胀处理- Otsu median filter automatically calculate threshold using 4 direction roberts and edge detection and binarization expansion deal(Otsu median filter automatically calculate threshold using 4 direction roberts and edge detection and binarization expansion deal)

说明：  这是一个实现图像缩放的MATLAB源代码，可以有用户自己调节缩放的倍数(This is an image scaling to achieve the MATLAB source code, users can zoom in multiples of its own regulation)

水平百叶窗显示图片，讲一张图像垂直分成若干份，bitblt一行一行的显示(The level of blinds show pictures, talk about an image vertically divided into several copies, bitblt display line by line)

实现了一个二维小波分解、重构程序，对一副16*16的数字图像进行了二层小波分解和重构。(A two-dimensional wavelet decomposition and reconstruction program is implemented, and a 16*16 digital image is decomposed and reconstructed by two-level wavelet.)

图像金字塔，得到降采样后的图像和残差图像(image pyramids,generating image pyramids and predicton residual)

二维离散分数阶傅里叶变换程序，可以用做图像处理，绝对可用(Two-dimensional discrete fractional Fourier transform program, you can use to do image processing, absolutely free)

基于MATLAB的图像二维熵的计算，采用了改进的均值滤波器模板，使结果与物理意义更加契合。(This program calculates the 2-dim entropy of images, which is based on MATLAB. It uses a better average filter template, and makes the result in accordance with the identification of entropy.)

说明：  UNet最早发表在2015的MICCAI上，短短3年，引用量目前已经达到了4070，足以见得其影响力。而后成为大多做医疗影像语义分割任务的baseline，也启发了大量研究者去思考U型语义分割网络。而如今在自然影像理解方面，也有越来越多的语义分割和目标检测SOTA模型开始关注和使用U型结构，比如语义分割Discriminative Feature Network(DFN)(CVPR2018)，目标检测Feature Pyramid Networks for Object Detection(FPN)(CVPR 2017)等。(Its influence has reached 70% in 2015. Then it became the baseline that most of the medical image semantic segmentation tasks, and inspired a large number of researchers to think about the U-shaped semantic segmentation network. In the aspect of natural image understanding, more and more SOTA models of semantic segmentation and object detection begin to pay attention to and use U-shaped structure, such as semantic segmentation, discriminative feature network (DFN) (cvpr2018), feature pyramid networks for object detection (FPN) (CVPR 2017), etc.)

说明：  图像预处理以及提取图像的Tamura 纹理特征(Image preprocessing and Tamura texture feature extraction)

脸部特征提取，特征点提取、划分特征点区域，这是在中医面诊课题中用到的代码(Facial feature extraction, feature extraction, feature points divided region)