I have implemented your suggestions and am returning for more advice. The longest exposure I used for my analysis was for 1 second. The FFT of the individual duplicate files does demonstrate a non-Gaussian component as shown below. The noise at this exposure level consists of shot noise and PRNU.
Indeed, the plot shows that there was a significant amount of low frequency signal (already visble in the FFT result).
However, when one subtracts the pair to eliminate fixed pattern noise, the FFT is essentially that of Poisson noise ...
Yes, there is no clear sign of boosted or suppressed frequencies (the horizontal spectrum is uniform with some random statistical sample fluctuations), the noise is pretty random as we can expect from purely Poisson/Gauss and White noise.
as demonstrated by the radial plot shown below in comparison to a Poisson generated file generated with ImageJ using a mean of 13085 which is the mean raw level of the files at that exposure level. The normalized integrated intensity is higher, since the SD of the subtracted images is for two images and was not divided by sqrt(2).
Yes, the benefit of comparing with the spectrum of a known (Poisson only) noise spectrum is that significant differences with the empirical spectrum of the subtracted images should be apparent. One improvement, but it won't change the outcome much, is that you can use the Modulatory Poisson noise option of RandomJ, since it will keep the average intensity of the original and add a proportional amount (sqrt(signal level)) of Poisson distributed noise (regardless of the mean value setting, it's based on image content levels).
I conclude that the raw files were filtered for PRNU and after this was removed, the files were suitable for analysis. Is this correct in your estimation?
It's hard to tell what caused it exactly, but there was a 'significant' amount of low frequency information still present in the individual Raw file(s). This is not necessarily different with other cameras, it's just there (maybe from the camera electronic circuits). That may be caused by periodic pattern noise, as evidenced in the FFT conversion mostly horizonal and vertical. It was not removed yet, because that would have lowered the spectral component in the radial profile plot.
Most of the signal is in the most central (approx. 25 pixel) radius of the FFT representation, and ImageJ allows to readout the spatial frequency (less than 20 pixels/cycle) when you hover the mouse over the FFT image. Maybe the readout circuits use 8 or 16 parallel rows or columns? Other than that, there does not seem to be any active frequency based noise reduction (no gradual or abrupt decline towards higher frequencies) that can be detected. I'm not sure if Hot Pixel Suppression can be spotted this way, because that is also pretty random across the image. Selecting a larger crop than 512x512 pixels will give more samples (and even lower frequencies) to analyse, but vignetting may start to influence the average signal level of single Raws, and gain can be evaluated at these levels.
After subtraction, the image data was purely random, as demonstrated by a uniform radial spectrum plot, and suitable for random (read+Poisson) noise analysis. That means that there should be no pattern noise influence in the images close to the clipping level.