Quote:
Originally Posted by Shiraz
just did a test.
stacked 11 subs with no alignment and then again with star alignment.

Actually, I hadn't thought of doing this! Despite being pretty terrible to look at, non aligned stacks are a good control in an experiment to compare against aligned stacks.
I picked a set of 50 luminance subs with plenty of light pollution and did a similar test. I found:
Non aligned stacks: Noise = Original Noise / SQRT(N) x 1.15
Aligned stacks: Noise = Original Noise / SQRT(N) x 0.74
Hence, I didn't achieve the normal SQRT(N) reduction in noise for non aligned stacks (maybe FPN becomes a factor).
However, I did "beat" the normal SQRT(N) reduction in aligned stacks, by 1/0.74 = 1.35  not quite the 1.5 SNR improvement I was looking for (again maybe FPN limits the noise reduction in larger stacks).
The important bit is that my noise in aligned stacks was lower than that in non aligned stacks by 1.15 / 0.74 = 1.55, like Ray reports, and close to the 1.5 in the theory of resampling after alignment.
In my experience anyway, I'd therefore go with a formula:
Stack SNR = Sub SNR / SQRT(N) x 1.35
But it is complicated by the impact of FPN in stacks and also by the difficulty of consistently measuring noise in images, and hence measuring SNR.
In the spreadsheet, I measured the noise by programmatically dividing the image into rectangles and doing a kappasigma standard deviation calculation (to stop stars affecting the noise calculation). I then picked a few rectangles manually in starless areas to manually verify the calculated noise.