Jack and Jim K,
How would these considerations affect my modeling of the Canon sensors using the model proposed by Roger Clark? I don't have matab and am not sufficiently versed in the fine points of probability distributions to carry out this analysis, but perhaps you guys can help. These considerations could well affect the determination of engineering DR, but would be less critical when one is dealing with practical photographic DR.
Hi Bill,
I don't think it will affect the DR assessment like you used much, unless one uses such an
extreme under-exposure that we get into those low e- counts that will (occasionally) start showing a larger difference between Poisson and Gaussian distributions. The real issue is that the Poisson statistics at low counts will develop a bias/asymmetry compared to a Gaussian, and they can be quite different from observation to observation. So one would need a large number of observations to make sense of it anyway.
The read-noise is more likely to have a Gaussian distribution, and the exposure a more Poisson distribution particularly when
very very low exposures are in play. But for developing a physics model like Jim and Jack did one would need to use the correct distribution model to avoid wrong conclusions over time (multiple observations).
Attached are the Probability Density Functions of the Poisson and the Normal distribution, at a mean of 5, 10 and 20.
Cheers,
Bart