4.3图像噪声

4.3.1 椒盐噪声

/////////////////////////////4.3.1椒盐噪声/////////////////////////////
////////https://blog.csdn.net/qq_35724402/article/details/60347024
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>

using namespace cv;
using namespace std;

//加入椒盐噪声
void salt(Mat image, int n)
{
    int i, j;
    for (int k = 0; k<n; k++)
    {
        // rand()是随机数生成器
        i = rand() % image.cols;
        j = rand() % image.rows;
        if (image.type() == CV_8UC1)
        { // 灰度图像
            image.at<uchar>(j, i) = 255;
        }
        else if (image.type() == CV_8UC3)
        { // 彩色图像
            image.at<cv::Vec3b>(j, i)[0] = 255;
            image.at<cv::Vec3b>(j, i)[1] = 255;
            image.at<cv::Vec3b>(j, i)[2] = 255;
        }
    }
}


int main()
{
    Mat image = imread("D:\彩色lena.jpg");
    if (image.empty())
    {
        cout << "Error!cannot be read...../n";
        return -1;
    }
    imshow("sourceimage", image);
    salt(image, 5000);
    //namedWindow("saltiamge");
    imshow("saltimage", image);
    waitKey(0);

}

参考：https://blog.csdn.net/qq_34784753/article/details/69379135

 4.3.2 高斯噪声

////////https://blog.csdn.net/qq_34784753/article/details/69379135
#include <cmath>
#include <limits>
#include <cstdlib>
#include <iostream>
#include <opencv2corecore.hpp>
#include <opencv2highguihighgui.hpp>

using namespace cv;
using namespace std;

double generateGaussianNoise(double m, double sigma);
Mat addGaussianNoise(Mat &srcImag);

int main()
{
    Mat srcImage = imread("D:\彩色lena.jpg");
    if (!srcImage.data)
    {
        cout << "读入图片错误！" << endl;
        system("pause");
        return -1;
    }
    imshow("原图像", srcImage);
    Mat dstImage = addGaussianNoise(srcImage);
    imshow("添加高斯噪声后的图像", dstImage);
    waitKey();
    return 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;
}

//为图像添加高斯噪声
Mat addGaussianNoise(Mat &srcImag)
{
    Mat dstImage = srcImag.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;
}