here's my problem: I'm trying to create a simple program which adds Gaussian noise to an input image. The only constraints are that the input image is of type CV_64F (i.e. double) and the values are and must be kept normalized between 0 and 1.
The code I wrote is the following:
Mat my_noise;
my_ noise = Mat (input.size(), input.type());
randn(noise, 0, 5); //mean and variance
input += noise;
The above code doesn't work, the resulting image doesn't get displayed properly. I think that happens because it gets out of the 0,1 range. I modified the code like this:
Mat my_noise;
my_ noise = Mat (input.size(), input.type());
randn(noise, 0, 5); //mean and variance
input += noise;
normalize(input, input, 0.0, 1.0, CV_MINMAX, CV_64F);
but it still doesn't work. Again, the resulting image doesn't get displayed properly. Where is the problem? Remember: the input image is of type CV_64F and the values are normalized between 0 and 1 before adding noise and have to remain like also after the noise addition.
Thank you in advance.
Your problem is that Gaussian noise can have arbitrary amplitude and can't be represented in [0, 1]. Renormalizing after adding the noise is a mistake, because just one large noise value could affect the whole image.
Probably what you need to do is saturate the image when adding the noise, values that would be greater than 1.0 are clamped to 1.0, and values that would be less than 0.0 are clamped to 0.0.
Something like
cv::Mat noise(input.size(), input.type());
cv::randn(noise, 0, 5); //mean and variance
input += noise;
cv::Mat clamp_1 = cv::Mat::ones(input.size(), input.type());
cv::Mat clamp_0 = cv::Mat::zeros(input.size(), input.type());
input = cv::max(input, clamp_0);
input = cv::min(input, clamp_1);
Also a noise variance of 5 is very large, it means that there is about a 92% chance that the input + noise will be outside the range [0, 1], assuming the input is uniformly distributed on [0, 1]. So your saturated image will be mostly black and white, with the input image having little effect on the result.