Adding Noise to Image - Opencv

edit

Indeed your issue was that you were not working with signed images. And in the following code you can find an alternative in order to avoid looping through the image pixels, which my guess would be that it is more time consuming (I have not test it to be honest) than using the internal functions of opencv:

void addGaussianNoise(Mat &image, double average=0.0, double standard_deviation=10.0)
{
    // We need to work with signed images (as noise can be
    // negative as well as positive). We use 16 bit signed
    // images as otherwise we would lose precision.
    Mat noise_image(image.size(), CV_16SC3);
    randn(noise_image, Scalar::all(average), Scalar::all(standard_deviation));
    Mat temp_image;
    image.convertTo(temp_image,CV_16SC3);
    addWeighted(temp_image, 1.0, noise_image, 1.0, 0.0, temp_image);
    temp_image.convertTo(image,image.type());
}

As you gaussian noise is ranging 0 to 255 you add energy to image I think you can substract mean to your result and it should be OK

Mat mSource_Bgr;
mSource_Bgr= imread("c:/Users/Laurent.PC-LAURENT-VISI/Downloads/ReaMu.png",CV_LOAD_IMAGE_ANYCOLOR);

double m_NoiseStdDev=10;

Mat mNoise_Bgr = mSource_Bgr.clone();
Mat mGaussian_noise = Mat(mSource_Bgr.size(),CV_8UC3);

randn(mGaussian_noise,0,m_NoiseStdDev);
double minIm,maxIm;

std::vector<Mat> plan;
split(mGaussian_noise,plan);
for (int i = 0; i < mGaussian_noise.channels(); i++)
{
minMaxIdx(plan[i],&minIm,&maxIm);
cout << minIm << "\t";
cout << maxIm << "\n";
}
mNoise_Bgr += mGaussian_noise;
Scalar x=mean(mGaussian_noise);
cout << x << "\n";
subtract(mNoise_Bgr,x,mNoise_Bgr);

imshow("Output Window",mNoise_Bgr);
waitKey();

Following remark from @LorenaGdL and @Balaji R I think this code is better (negative value at the end are not process and it could be a problem) :

Mat mSource_Bgr;
double minIm,maxIm;
std::vector<Mat> plan;
Scalar x;

mSource_Bgr= imread("ReaMu.png",CV_LOAD_IMAGE_ANYCOLOR);
cout << "Original image\n";
split(mSource_Bgr,plan);
for (int i = 0; i < mSource_Bgr.channels(); i++)
{
    minMaxIdx(plan[i],&minIm,&maxIm);
    cout << minIm << "\t";
    cout << maxIm << "\n";
}
x=mean(mSource_Bgr);
cout << x << "\n";


double m_NoiseStdDev=10;

Mat mNoise_Bgr ;
mSource_Bgr.convertTo( mNoise_Bgr,CV_32FC3, 1,0);


Mat mGaussian_noise = Mat(mSource_Bgr.size(),CV_32FC3);

randn(mGaussian_noise,0,m_NoiseStdDev);
mNoise_Bgr += mGaussian_noise;


cout << "Gaussian noise\n";
split(mGaussian_noise,plan);
for (int i = 0; i < mGaussian_noise.channels(); i++)
{
    minMaxIdx(plan[i],&minIm,&maxIm);
    cout << minIm << "\t";
    cout << maxIm << "\n";
}
x=mean(mGaussian_noise);
cout << x << "\n";

cout << "Result image+ gausssian noise\n";
split(mNoise_Bgr,plan);
for (int i = 0; i < mNoise_Bgr.channels(); i++)
{
    minMaxIdx(plan[i],&minIm,&maxIm);
    cout << minIm << "\t";
    cout << maxIm << "\n";
}
x=mean(mNoise_Bgr);
cout << x << "\n";

Mat result;
mNoise_Bgr.convertTo(result,CV_8UC3,1,0);
imshow("Output Window",result);
cout << "Image with noise\n";
split(result,plan);
for (int i = 0; i < mGaussian_noise.channels(); i++)
{
    minMaxIdx(plan[i],&minIm,&maxIm);
    cout << minIm << "\t";
    cout << maxIm << "\n";
}
x=mean(result);
cout << x << "\n";

waitKey();