Episode Eight：opencv2.4.9实现向图像添加噪声和对图像滤波去噪

￡神魔★判官ぃ 2022-06-04 10:12 198阅读 0赞

该篇章主要实现了向一幅图像中分别加入椒盐噪声和高斯噪声，体会两种不同噪声的区别；并通过三种线性邻域滤波（方框滤波，均值滤波和高斯滤波）和两种非线性滤波（中值滤波和双边滤波）对图像进行去噪。在其中运用轨迹条进行调节参数以达到最优的滤波效果。

code:

#include <opencv2/highgui/highgui.hpp>
    #include <opencv2/core/core.hpp>
    #include <opencv2/opencv.hpp>
    //#include <opencv2/imgproc/imgproc.hpp>
    #include <iostream>
    
    
    using namespace std;
    using namespace cv;
    
    Mat imagey1,image,g_image4, g_image5;
    int g_nMedianBlurValue = 10;//中值滤波参数值
    int g_nBilateralFilterValue = 10;//双边滤波参数值
    
    Mat addSaltNoise(const Mat srcImage, int n); //添加椒盐噪声
    double generateGaussianNoise(double mu, double sigma); //生成高斯噪声								   
    Mat addGaussianNoise(Mat & srcImage);  //给图像添加高斯噪声
    
    //轨迹条回调函数
    static void on_MedianBlur(int, void *);   //中值滤波器
    static void on_BilateralFilter(int, void *);   //双边滤波器
    
    
    int main()
    {
    	/*Mat src = imread("pic/test1/a.jpg");
    	Canny(src,src,3,9,3);
    	namedWindow("【效果图】Canny边缘检测", WINDOW_NORMAL);
    	imshow("【效果图】Canny边缘检测", src);
    	waitKey();*/
    	
    	imagey1 = imread("pic/human1.jpg");
    	if (!imagey1.data)
    	{
    		cout << "图像读取失败！" << endl;
    	}
    	namedWindow("原图");
    	imshow("原图", imagey1);
    	
    	/*image = addSaltNoise(imagey1, 3000);//给图像添加椒盐噪声
    	namedWindow("添加椒盐噪声的图像");
    	imshow("添加椒盐噪声的图像", image);*/
    	image = addGaussianNoise(imagey1);//给图像添加Gaussian噪声
    	namedWindow("添加Gaussian噪声的图像");
    	imshow("添加Gaussian噪声的图像", image);
    
    	g_image4 = image.clone();
    	g_image5 = image.clone();
    
    	Mat out1,out2,out3;
    	
    
    	线性滤波
    	boxFilter(image, out1, -1, Size(5, 5)); //方框滤波
    	namedWindow("1方框滤波处理图像");
    	imshow("1方框滤波处理图像", out1);
    
    	blur(image, out2, Size(5, 5));       //均值滤波
    	namedWindow("2均值滤波处理图像");
    	imshow("2均值滤波处理图像", out2);
    
    	GaussianBlur(image, out3, Size(3, 3), 0, 0);   //高斯滤波
    	namedWindow("3Gaussian滤波处理图像");
    	imshow("3Gaussian滤波处理图像", out3);
    	
    	非线性滤波
    	namedWindow("4中值滤波处理图像");  //中值滤波
    	//创建轨迹条
    	createTrackbar("参数值：","4中值滤波处理图像",&g_nMedianBlurValue,50,on_MedianBlur);
    	on_MedianBlur(g_nMedianBlurValue, 0);
    
    	namedWindow("5双边滤波处理图像");  //双边滤波
    	//创建轨迹条
    	createTrackbar("参数值：", "5双边滤波处理图像", &g_nBilateralFilterValue, 50, on_BilateralFilter);
    	on_BilateralFilter(g_nBilateralFilterValue, 0);
    
    	waitKey();
    	return 0;
    }
    
    
    static void on_MedianBlur(int, void *)  //中值滤波
    {
    	medianBlur(image, g_image4, g_nMedianBlurValue * 2 + 1);
    	imshow("4中值滤波处理图像", g_image4);
    }
    
    static void on_BilateralFilter(int, void *)  //双边滤波
    {
    	bilateralFilter(image, g_image5, g_nBilateralFilterValue, g_nBilateralFilterValue * 2, g_nBilateralFilterValue / 2);
    	imshow("5双边滤波处理图像", g_image5);
    }
    
    Mat addSaltNoise(const Mat srcImage, int n)
    {
    	Mat dstImage = srcImage.clone();
    	for (int k = 0; k < n; k++)
    	{
    		//随机取值行列
    		int i = rand() % dstImage.rows;
    		int j = rand() % dstImage.cols;
    		//图像通道判定
    		if (dstImage.channels() == 1)
    		{
    			dstImage.at<uchar>(i, j) = 255; //盐噪声
    		}
    		else
    		{
    			dstImage.at<Vec3b>(i, j)[0] = 255;
    			dstImage.at<Vec3b>(i, j)[1] = 255;
    			dstImage.at<Vec3b>(i, j)[2] = 255;
    		}
    	}
    	for (int k = 0; k < n; k++)
    	{
    		//随机取行列
    		int i = rand() % dstImage.rows;
    		int j = rand() % dstImage.cols;
    		//图像通道判定
    		if (dstImage.channels() == 1)
    		{
    			dstImage.at<uchar>(i, j) = 0;//椒噪声
    		}
    		else
    		{
    			dstImage.at<Vec3b>(i, j)[0] = 0;
    			dstImage.at<Vec3b>(i, j)[1] = 0;
    			dstImage.at<Vec3b>(i, j)[2] = 0;
    		}
    	}
    	return dstImage;
    }
    
    //生成高斯噪声
    double generateGaussianNoise(double mu, double sigma)
    {
    	//定义小值
    	const double epsilon = numeric_limits<double>::min();
    	static double z0, z1;
    	static bool flag = false;
    	flag = !flag;
    	//flag为假构造高斯随机变量x
    	if (!flag) 
    	{
    		return z1*sigma + mu;
    	}
    	double u1, u2;
    	//构造随机变量
    	do
    	{
    		u1 = rand()*(1.0 / RAND_MAX);
    		u2 = rand()*(1.0 / RAND_MAX);
    	} while (u1 <= epsilon);
    	//flag为真构造高斯随机变量
    	z0 = sqrt(-2.0*log(u1))*cos(2 * CV_PI*u2);
    	z1 = sqrt(-2.0*log(u1))*sin(2 * CV_PI*u2);
    	return z0*sigma + mu;
    }
    
    //给图像添加高斯噪声
    Mat addGaussianNoise(Mat & srcImage)
    {
    	Mat dstImage = srcImage.clone();
    	int channels = dstImage.channels();
    	int rowsNumber = dstImage.rows;
    	int colsNumber = dstImage.cols*channels;
    	//判断图像的连续性
    	if (dstImage.isContinuous())
    	{
    		colsNumber *= rowsNumber;
    		rowsNumber = 1;
    	}
    	for (int i = 0; i < rowsNumber; i++)
    	{
    		for (int j = 0; j < colsNumber; j++)
    		{
    			//添加高斯噪声
    			int val = dstImage.ptr<uchar>(i)[j] + generateGaussianNoise(2, 0.8) * 32;
    			if (val < 0) val = 0;
    			if (val > 255) val = 255;
    			dstImage.ptr<uchar>(i)[j] = (uchar)val;
    		}
    	}
    	return dstImage;
    }

测试结果：

原图像：

![SouthEast][]

添加椒盐噪声后的结果：

![SouthEast 1][]

添加高斯噪声后的结果：

![SouthEast 2][]

带轨迹条的滤波：

![SouthEast 3][]

**附：**

几种常见颜色对应的RGB数值：

![SouthEast 4][]

Smart is the new sexy!

[SouthEast]: /images/20220604/1fb5a43352e1468cb2830777522ca6b9.png
[SouthEast 1]: /images/20220604/ac43d81c539f48c1a7051bc2b39372f4.png
[SouthEast 2]: /images/20220604/48e837c5d7564f9c847242281941a9fa.png
[SouthEast 3]: /images/20220604/474ccb4ac60548eea3c5fb35ee473d87.png
[SouthEast 4]: /images/20220604/d6a81901d4ea4bd1a953ef3b8308a3a5.png