Suavi,
Quote:
Originally Posted by Slawomir
I have checked single frames with the method from the link you provided, and these are noise estimates (biweight midvariance) I got:
Single un-calibrated sub: 0.9828
MB and Flat calibrated: 0.9842
SB and Flat calibrated: 0.9851
I am assuming the smaller the number the better, so these results also indicate that I would be better off with Master Bias (and possibly could add more bias frames to it, as well as it probably would not hurt to take a few more dozens of flats).
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Yes, the numbers are scaled noise estimates so smaller is better. Because they are estimates you should take them with a grain of salt. I'd run several examples and check for consistent behaviour.
Quote:
Originally Posted by Slawomir
EDIT: Possibly a silly question...in PI, would overscan only apply to calibrating Bias frames (BatchPreProcessing Script)? And can I collect new bias frames with overscan enabled and use them to calibrate Lights and Flats that were captured without overscan enabled?
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Sorry, overscan calibration is applied to the calibration frames and the lights as well, so they all need to include the overscan area. You can't apply it to old data which doesn't include it.
Probably best to check your camera for drift first. If the bias is stable then you won't gain anything except additional hassle by overscan calibration
Cheers,
Rick.