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ROF-L2去噪模型，可以有效的去除加性噪声(ROF-L2 denoising model)

《智能图像处理技术》——由李弼程等编写，区别于大多传统的图像处理技术书籍，该书主要讨论了图像处理的最新发展，如图像的特征分析、配准、融合、分类等技术，具有前沿性和先进性( Intelligent image processing technology - such as prepared by Bicheng, mostly from the traditional image processing technology books, the book discusses the latest developments in image processing, such as the characteristics of image analysis, registration, integration, classification and other technology, having cutting-edge and advanced)

该文件涵盖了65个使用IDL进行图像处理的源码例子，对IDL语言以及ENVI软件的理解非常有帮助。(The document covers 65 for image processing using IDL source code examples, on IDL and ENVI software' s understanding of language is very helpful.)

可以实现对交通视频中运动车辆的检测和目标的提取，只需要稍作修改即可使用(You can achieve the target detection and extraction of moving vehicles in traffic video, you only need minor modifications to use)

这是清华大学章毓敏写的图像工程，分上中下三册，希望对学习图像处理的学者有所帮助。(This is the engineering of Tsinghua University Zhang Yumin, write the image points on the three, hoping to help scholars learn image processing.)

该程序利用小波模极大值对图像边缘进行检测并提取,效果很理想(The program using the wavelet modulus maxima of the image edge detection and extraction, the effect of very satisfactory)

首先通过建立一个可靠的背景更新模型，然后在视频序列中取一帧图像与背景做差得到完整可靠的运动目标图像(This program is used to detect the moving targets by image substraction ,)

说明：  灰度不均匀图像处理，具有优良效果，可以使用，效果较好(Uneven grayscale image processing, with excellent results, can be used, with better results)

利用小波变换的去噪程序！和中值滤波进行对比！(The use of wavelet transform denoising procedure! And median filtering comparison!)

本算法在Matlab2008b 环境下实现。包括main,san 和ssd 三个函数。 这次实现的算法并不是比较两个已经知道的点是否匹配，而是已知一个图形中的特征点，在另外一个图像中找到与其最匹配的点，匹配度用SAD 或者SSD 来度量。 main.m 是程序的入口，包括生成和读入实验数据，调用sad 函数和ssd 函数求匹配点，最后绘 制出最后的结果。sad.m 是用sad 度量方法在另一图中求解匹配点的函数实现，有3 个参数 y=sad(x,image1,image2)。其中x 是代匹配的数据，image1 是图像1 数据，image2 是图像2 数据。 从理论上分析，ssd 比sad 算法要复杂一点，经过测试，在一幅640*480 的图像中寻找10 个匹配点数据SAD 用时25.062519 秒，SSD 用时25.291432 秒。(The algorithm Matlab2008b environment to achieve. Including the main, san, and ssd three functions. The implementation of the algorithm is not the point of comparing two matches already know, but the known feature points in a graphic, an image found in the other match with the most points, matching measured with the SAD or SSD. main.m is the entry, including the generation and reading test data, call the sad ssd function evaluation functions and matching points, and finally draw the final result. sad.m measure is sad figure in another match point in the function implementation to solve, there are three parameters y = sad (x, image1, image2). Where x is the generation of matching data, image1 is the data image 1, image2 is the image 2 data. From the theoretical analysis, ssd little more complicated than the sad algorithm, tested in a 640* 480 images of 10 match points in the search for data using time 25.062519 seconds SAD, SSD with time 25.291432 seconds. )