引导滤波

实现了一个图像分块的DCT去噪算法，用于学习参考(We achieve an image block DCT denoising algorithm, for learning reference)

说明：  IDL读取txt，txt第一列是纬度，第二列是经度，第三列是DN值， 坐标转行列号 ，并以图像的形式输出(IDL reads TXT. The first column of TXT is latitude, the second column is longitude, and the third column is DN value. The coordinate turns the line and column number and outputs it in the form of image)

应用小波变换 模极大值算法实现医学图像（CT图像和MRI图像）的融合(Wavelet transform modulus maxima to achieve medical image fusion)

imx377 手册 imx377 datasheet imx377 datasheet(imx377datashee, imx377datasheet,imx377datasheet)

c源代码 图象的压缩编码，JPEG压缩编码标准 功能是行程编码，JPEG压缩编码（基本系统）(c code, function: jpeg stand for image show)

采用色温估计法做的白平衡操作，之前是打算用到水下处理的，当然这个算法和PS的白平衡比有点LOW，可是这个能够matlab，有一定用途(The white balance operation using the color temperature estimation method was intended to be used underwater. Of course, the white balance ratio of this algorithm and PS is a little LOW, but this can be used in matlab.)

利用opencv高斯混合背景建模，并进行开闭运算滤波， 提取视频监控中的车辆(Using opencv Gaussian mixture background modeling and opening and closing operation filtering to extract vehicles in video surveillance)

在数字图像处理中，灰度直方图是最简单且最有用的工具，可以说，对图像的分析与观察直到形成一个有效的处理方法，都离不开直方图。(in digital image processing, the histogram is the simplest and most useful tool, it can be said that the right image analysis and observation until the formation of an effective treatment method has been based on histogram.)

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.)

解决图像的DCT变换与逆变换，为JEPG压缩做铺垫(To solve the image DCT transform and inverse transform, in order to pave the way for JEPG compression)