图像处理：加入椒盐和高斯噪声(C++实现)

调用方式

Mat img, img1,img2;
addSaltNoise(img,img1,300);//添加椒盐噪声
ddGaussianNoise(img,img2,0,1);//添加高斯噪声（均值=0，方差=1）

函数

//生成随机椒盐噪声
void addSaltNoise(const Mat &srcImage, Mat &dstImage, int n)
{
	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) = 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;
		}
	}
}
//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;
}
//为图像加入高斯噪声
void addGaussianNoise(Mat &srcImag, Mat &dstImage, double mu, double sigma)
{
	dstImage = srcImag.clone();
	int channels = dstImage.channels();
	int rowsNumber = dstImage.rows;
	int colsNumber = dstImage.cols*channels;
	//推断图像的连续性
	if (dstImage.isContinuous())
	{
		colsNumber *= rowsNumber;
		rowsNumber = 1;
	}
	for (int i = 0; i < rowsNumber; i++)
	{
		for (int j = 0; j < colsNumber; j++)
		{
			//加入高斯噪声
			int val = dstImage.ptr<uchar>(i)[j] +
				generateGaussianNoise(mu, sigma) * 32;
			if (val < 0)
				val = 0;
			if (val>255)
				val = 255;
			dstImage.ptr<uchar>(i)[j] = (uchar)val;
		}
	}
}