添加高斯噪声、椒盐噪声与均值、中值滤波处理

#include 
#include 
#include 
#include 
#include 
#include 
#include 
using namespace cv;
int Rows;//图像的行数
int Cols;//图像的列数

//生成高斯噪声
double generateGaussianNoise(const double &sigma,const double & mu)
{
	const double epsilon = std::numeric_limits<double>::min();//返回目标数据类型能表示的最逼近1的正数和1的差的绝对值
	static double z0, z1;
	//构造随机变量
	double u0, u1;
	do
	{
		u0 = rand()*(1.0 / RAND_MAX);
		u1 = rand()*(1.0 / RAND_MAX);
	} while (u0 <epsilon||u1<epsilon);

	z0 = sqrt(-2.0*log(u0))*cos(2 * CV_PI*u1);
	//z1 = sqrt(-2.0*log(u0))*sin(2 * CV_PI*u1);
	return z0 * sigma + mu;
}
//给图像添加高斯噪声
Mat addGaussianNoise(const Mat &grayImage)
{
	double sigma=2.0; //方差
	double mu=1;   //均值
	double k=10;  //比例系数
	Mat resultImage = grayImage.clone();   //图像的拷贝
	for (int i = 0; i < Rows; i++)
	{
		for (int j = 0; j < Cols; j++)
		{
			double temp= resultImage.at<uchar>(i, j) + generateGaussianNoise(sigma,mu) * k;
			if (temp < 0)
				temp= 0;
			if (temp> 255)
				temp= 255;
			resultImage.at<uchar>(i, j) = (uchar)temp;
		}
	}
	return resultImage;
}
//给图像添加椒盐噪声
Mat addSaltPepperNoise(const Mat &grayImage)
{
	Mat resultImage = grayImage.clone();   //图像的拷贝
	int sp = Rows * Cols;
	double snr = 0.9;
	int np = int(sp * (1 - snr));
	//std::cout << np << std::endl;
	for (int i = 0; i < np; i++)
	{
		//产生随机的位置
		int randx = rand()%Rows;
		int randy = rand()%Cols;
		int noise = rand() % 2;
		if (noise)
		{
			resultImage.at<uchar>(randx, randy) = 255;
		}
		else
		{
			resultImage.at<uchar>(randx, randy) = 0;
		}

	}
	return resultImage;
}
//给图像添加随机白噪声
Mat addRandWhiteNoise(const Mat &grayImage)
{
	Mat resultImage = grayImage.clone();   //图像的拷贝
	int sp = Rows * Cols;
	double snr = 0.8;
	int np = int(sp * (1 - snr));
	//std::cout << np << std::endl;
	for (int i = 0; i < np; i++)
	{
		//产生随机的位置
		int randx = rand() % Rows;
		int randy = rand() % Cols;
		resultImage.at<uchar>(randx, randy) = 255;

	}
	return resultImage;
}
//均值滤波
Mat meanFilter(const Mat &imageNoise,int m,int n)
{
	Mat resultImage = imageNoise.clone();
	//图像的边缘不做处理
	for (int i = m / 2; i < Rows - m / 2; i++)
	{
		for (int j = n / 2; j < Cols - n / 2; j++)
		{
			double temp = 0;
			for (int x = i - m / 2; x <= i + m / 2; x++)
			{
				for (int y = j - n / 2; y <= j + n / 2; y++)
				{
					temp += imageNoise.at<uchar>(x, y);
				}
			}
			resultImage.at<uchar>(i, j) = temp / (m*n);
		}
	}
	return resultImage;
}
//中值滤波
Mat medianFilter(const Mat &imageNoise,int m,int n)
{
	Mat resultImage = imageNoise.clone();
	for (int i = m / 2; i < Rows - m / 2; i++)
	{
		for (int j = n / 2; j < Cols - n / 2; j++)
		{
			std::vector<double>getNearPixel;
			double temp;
			for (int x = i - m / 2; x <= i + m / 2; x++)
			{
				for (int y = j - n / 2; y <= j + n / 2; y++)
				{
					temp=imageNoise.at<uchar>(x, y);
					getNearPixel.push_back(temp);
				}
			}
			std::sort(getNearPixel.begin(), getNearPixel.end());
			resultImage.at<uchar>(i, j) = getNearPixel[m*n/2];
		}
	}
	return resultImage;
}
int main()
{
	using namespace cv;
	//读入图像并创建图像矩阵
	Mat srcImage = imread("D:\\figure\\lena2.jpg");
	Mat grayImage;
	cvtColor(srcImage, grayImage, COLOR_BGR2GRAY);
	//imshow("grayImage", grayImage);
	//waitKey(0);
	Rows = grayImage.rows;//行数
	Cols = grayImage.cols;//列数

/*添加噪声处理*/
	//添加高斯噪声
	//Mat imageAddGn = addGaussianNoise(grayImage);
	//imshow("imageGuassionNoise", imageAddGn);
    //waitKey(0);

	//添加椒盐噪声
	//Mat imageAddSp = addSaltPepperNoise(grayImage);
	//imshow("addSaltPepperNoise", imageAddSp);
	//waitKey(0);

	//添加随机白噪声
	Mat imageAddRw = addRandWhiteNoise(grayImage);
	//imshow("addRandWhiteNoise",imageAddRw);
	//waitKey(0);

//去除噪声处理
	//去除高斯噪声
	//Mat imageDelGn1 = meanFilter(imageAddGn, 3, 3);
	//Mat imageDelGn2 = medianFilter(imageAddGn, 5, 5);
	//imshow("imageDelGn",imageDelGn2);
	//waitKey(0);

	//去除椒盐噪声
	//Mat imageDelSp1 = meanFilter(imageAddSp, 5, 5);
	//Mat imageDelSp2 = medianFilter(imageAddSp, 3, 3);
	//imshow("imageDesSp",imageDelSp2);
	//waitKey(0);

	//去除随机白噪声
	//Mat imageDelRw1 = meanFilter(imageAddRw, 5, 5);
	Mat imageDelRw2 = medianFilter(imageAddRw, 5, 5);
	imshow("imageDesRw", imageDelRw2);
	waitKey(0);
	return 0;
}