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本程序是基于web图像的超分辨率重建，借鉴参考了其他人的算法(This procedure is based on the super image resolution of web image reconstruction, reference to other people s algorithm)

经典的K均值聚类，源码加利用三维高斯简单数据的实现对三类数据的聚类(Achieve the effect of classical K-means clustering, plus simple data)

亚像素提取边缘源代码，可以进行准确的边缘检测(Subpixel edge source code)

说明：  这是“Implicit Active Contours Driven by Local Binary Fitting Energy”（简称LBF模型）的MATLAB源代码。LBF模型是非常重要局部区域活动轮廓模型，它被广泛使用于各个领域，如MRI大脑图像分割，血管图像分割，图像偏差场纠正。(This is the "Implicit Active Contours Driven by Local Binary Fitting Energy" (referred to as the LBF model) of the MATLAB source code. LBF model is very important to local area active contour model, which is widely used in various fields, such as MRI brain image segmentation, vessel segmentation, image bias field correction.)

16QAM-matlab simulation,16QAM-matlab simulation,(16 qam- matlab simulation, 16 qam- matlab simulation,)

说明：  序贯重要性采样——标准粒子滤波算法。可直接使用。(Sequential importance sampling- standard particle filter. Can be used directly.)

根据RBM深度网络（Hinton，2006）进行MINST手写文字的分类器的训练，利用训练得到的权值制作了这个小程序，通过这个程序可以看出训练结果对数据集内的测试样本和训练样本都能进行很好的识别，但是对其他的手写字体识别就没有那么好了。(According to RBM depth network (Hinton, 2006) conducted MINST handwritten text classifier training, using the training to get the right value to produce this small program, this program can be seen through the results of the training data set and the training sample test samples are can be well identified, but on the other handwriting recognition is not so good.)

阴影消除matlab代码，运行可用，可供参考(The shadow eliminates the matlab code, which is available for reference)

《数字图像处理（第三版）》课后习题答案，作者冈萨雷斯(Answers to Exercises after Class of Digital Image Processing (Third Edition),author Gonzalez)

针对图像秘密共享前需要像素置乱的问题，基于(t, n)门限多重秘密共享思想，提出一种免置乱的图像秘密共享方案，以第n+1人的秘密份额作为因子参与秘密分割与重构，加大相邻像素之间的差距(Based on (t, n) threshold multiple secret sharing ideas, and put forward a kind of scrambling image from secret sharing scheme to n+ 1 people share the secret as factors involved in secret segmentation and reconstruction, increase the gap between adjacent pixels)