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混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。(Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of)

冈萨雷斯数字图像处理第二版中的tofloat函数，原书配带的函数式p文件，此处已经转为M文件。(Gonzalez Digital Image Processing Second Edition tofloat function, a function of the original book equipped with p-type documents, here has been converted to M-file.)

对图像的融合结果进行评价，有均值，标准偏差，熵，梯度，相关系数，光谱扭曲度(Image fusion results of the evaluation, there is the mean, standard deviation, entropy, gradient, correlation coefficient, spectral degree twist)

可以使用贝叶斯阈值估计来对噪声进行处理，效果很明显，可以使用(Bayesian threshold estimation can be used to deal with noise, the effect is obvious, you can use)

根据CVPR最佳论文实现的基于暗通道的图像去雾算法（含soft matting），以及算法改进：包括对色彩失真的改进以及一种达到soft matting效果的快速算法(According CVPR Best Paper realized based on dark channel images to fog algorithms (including soft matting), and algorithm improvements: Improvements include color distortion as well as a way to achieve fast algorithm for soft matting effect)

利用最大类间方差算法求取自动阈值，实现图像分割(Using the maximum inter class variance algorithm for automatic threshold, image segmentation)

运用基于粒子滤波法和MEANSIFT算法实现视频序列的目标跟踪(The use of particle filter based algorithm and MEANSIFT tracking video sequences)

hough变换检测直线代码在matlab下的运行(hough transformation)

说明：  用C语言实现图像的平滑，锐化及双线性内插算法的实现，效果很好(Using C language to achieve image smoothing, sharpening and bilinear interpolation algorithm to achieve good results)

使用二维双密度小波2-D Double-Density DWT Thresholding Method，决定图像滤波门限值，实现图像去噪处理。(use of two-dimensional density Wavelet 2-D Double-Density DWT Thresho lding Method, the decision threshold image filtering, image denoising.)