The question is a bit too vague to be precisely answered. Image noise typically have some specific probability-distribution, possibly in addition to dicretization-noise.
Typically it is possible to model photon-counting statistics as a random-number from a Poisson-distribution where the expected value of the photon-count is the Poisson-parameter Lambda:
D = get(get(gca,'Children'),'CData');
It should be possible to gather some tendensies from this simple example. I also urge you to look at the distribution of the residuals, for example:
From there you can/should get the courage to use the central limit theorem (surely everyones favourite statistical theorem) to dare to approximate the random-noise from something as demanding as a Poisson-process to the simple addition of a zero-centred normal-distributed random number with the standard deviation of the square-root of the expected image intensity.
After that one might start to faff-about with discretization-noise - which should not be ignored with images with 8-bit depth.