频域滤波器

说明：  模拟退火算法在图像处理中的典型应用，用matlab编程(Simulated annealing algorithm in a typical image processing applications, programming with matlab)

对超声图像的去噪和分割，可以显示肿瘤图像的边缘和面积(For ultrasound image denoising and segmentation, image can show the tumor edge and area)

先用PCA降维，在利用支持向量机进行分类，这个分类是二分类，所以PCA的降维降到两维即可分类。(Firstly, PCA dimensionality reduction is used to conduct classification with support vector machine. This classification is binary classification, so the dimensionality reduction of PCA can be reduced to two dimensions for classification.)

VisualC++数字图像处理技术详解第2版光盘-第二章(VisualC++ digital image processing technology Detailed Version 2 CD- Chapter 2)

说明：  再来一个绘图程序，花了我一个上午时间才搞定。(then a drawing program, I spent a morning time to get.)

该算法实现了数字图像中松弛标记算法处理图像。(The relaxation labeling algorithm in digital image processing)

模糊核聚类及几篇文章，用于数据和图像的模糊聚类分割，效果还行(nuclear fuzzy clustering and articles for data and image segmentation fuzzy clustering, the results were OK)

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)

使用帧差法、均值法和混合高斯模型对背景进行去除、提取前景信息。来源于2017年研究生数学建模比赛D题。(The background information is removed and the foreground information is extracted by frame difference method, mean value method and hybrid Gauss model. D graduate from the 2017 mathematical modeling contest.)

说明：  利用小波双线性超分辨率重建算法得到的重建图像会出现低、高频系数不匹配的现象，从而使得到的高分辨率图像灰度偏移。本文对该方法进行了改进，并引入局部适应插值得到更为理想的重建算法。即小波与局部适应插值结合算法。(Using Wavelet bilinear super-resolution reconstruction algorithm is the reconstructed image will appear low, high-frequency coefficients does not match the phenomenon, thereby enabling high-resolution image to be offset. In this paper, the method is improved, and the introduction of local interpolation to be better adapted to the reconstruction algorithm. That is, wavelet interpolation combined with the local adaptation algorithm.)