Retinex-of-image-enhancement

说明：  基于小波变换的图像压缩和编码。matlab实现。(Based on wavelet transform image compression and coding. matlab achieve.)

说明：  图像处理案例，本程序用matlab实现基于阈值分割的车牌定位识别功能(Image processing case, this program uses MATLAB to achieve license plate location and recognition function based on threshold segmentation.)

MRF代码实现,visual C++ 代码，不错，可以尝试(an implementation of MRF)

说明：  包含主要的相位恢复算法，用于解决相位恢复问题，在计算光学成像领域被广泛使用。(This code includes conventional phase retrieval algorithms designed for phase retrieval problems, which is widely used in computational optical imaging.)

一幅大小为256×256的256级灰度图像（每像素8bit） 如果图像的各个像素之间是统计独立的，求该图像的信息熵 -离散无记忆信源 如果图像的各个像素之间不是统计独立的，求该图像的信息熵 -图像像素的概率分布模型(A 256 gray scale image (8bit per pixel) with a size of 256 x 256 If each pixel of the image is statistically independent, the information entropy of the image is calculated Discrete memoryless source If each pixel of the image is not statistically independent, the information entropy of the image is calculated Probability distribution model of image pixels)

采用了小波变换的方法实现了红外光图像与可见光图像的融合，并且设计了一个简单的matlab GUI界面。小波变换的分解层数为2层，采用低频平均高频取大的融合规则，融合效果非常欢迎下载。(The method of wavelet transform is used to realize the fusion of infrared and visible light images, and a simple matlab GUI interface is designed. The number of decomposition layers of the wavelet transform is 2 layers, and the fusion rule is adopted by the low frequency average high frequency. The fusion effect is very welcome to download.)

说明：  基础SAD算法代码，最基本的图像匹配处理方法，也是最常用的基本法，有助于新手理解(Basic sad algorithm code)

C++ GUI Programming with Qt 3不用再详细解释了吧，下载试一下就知道了(C GUI Programming with Qt 3 do not have a detailed explanation of it, Download trial will know what)

使用OpenCV实现特征点的查找以及两幅图像中的特征点匹配(Use the function findHomography to find the transform between matched keypoints. Use the function perspectiveTransform to map the points)

数字图像处理(Digital Image Processing)是通过计算机对图像进行去除噪声、增强、复原、分割、提取特征等处理的方法和技术。 [1] 数字图像处理的产生和迅速发展主要受三个因素的影响：一是计算机的发展；二是数学的发展（特别是离散数学理论的创立和完善）;三是广泛的农牧业、林业、环境、军事、工业和医学等方面的应用需求的增长(Digital Image Processing (DIP) is a method and technology of removing noise, enhancing, restoring, segmenting and extracting features from images by computer. [1] The emergence and rapid development of digital image processing are mainly influenced by three factors: first, the development of computer; second, the development of mathematics (especially the establishment and improvement of discrete mathematics theory); third, the growth of the demand for extensive applications in agriculture, animal husbandry, forestry, environment, military, industry and medicine.)