That is a neat trick!
What are your opinions about the exposure-averaging parts of the article? Assuming a high-illumination, (relatively) short exposure environment like the one we’re interested in, it seems like our use case would be closer to the “Read Noise Limited” side of all those graphs. (I got lucky and my Gen3 Pregius sensor is actually listed in the graphs, so I don’t have to even guess about it.)
I didn’t have the mathematical foundation they built up in the article, but my own testing has shown that averaging ten or more exposures reduces sensor noise dramatically. I hadn’t gotten as far as quantifying it yet, but the noise in a single-exposure image vs. an average of many exposures is readily apparent just by looking at them side-by-side.
That’s 10x (nearest-neighbor) zoom of neutral gray captures. The difference between averaging 10 or 20 exposures is imperceptible, at least to my eye. When the time came, I was going to try and find the sweet spot by graphing the standard deviation across a wide range of exposure count. I’m guessing the best tradeoff between total capture time vs. sensor noise will be in the single digits for number of exposures.
In any event, for non-continuous scanners, this seems like one of the easiest wins for squeezing out an extra dB or two of SNR (assuming a vibration-free environment).