[Stonius (Markus)]

Hi all,

I wrote a really long post before, but deleted it.

Basically, what I'm trying to figure out is if you take a dark and bias integration, then run them through calibration, should the statistics on the resultant frame match those same values mathematically subtracted in a spreadsheet?

Being integrations, we are examining the non-random elements of the noise.

In experimenting with noise reduction I'm not sure if I should use a bias integration to play with or a superbias.

I'm finding frames calibrated with the superbias are *higher in both mean levels and StdDev, which I'm guessing is a result of the Superbias being consistently lower in mean and StdDev levels. But that seems counter-intuitive - I thought the point of a superbias was that it got rid of more noise?

Markus

PS, I'm aware that biases with the 1600 are not real biases. I achieved them by integrating 2.5s exposures, doing a linear regression and scaling the frame to the y crossing in pixinsight.