Quote:
Originally Posted by Slawomir
Hi Lee,
Interesting results indeed.
I would be careful though when comparing StDev for the entire images and using it as a measure of noise. Dark subtracted image will have lower mean ADU value, and that could affect the StDev? Perhaps instead of comparing StDev for the entire images, pick a smallish area with background only, if possible, and check StDev for this area in both images.
Anyway, whatever technique works and if you are ending up with cleaner images, that's great  |
Hmm, fair enough. Hard to find background in the data set that I have unfortunately since it's full of faint nebulosity. Cheers mate!
Quote:
Originally Posted by Shiraz
since your NB lights are almost identical to your darks (with some added nebula and stars, but with very little background sky), getting rid of dark Fixed Pattern Noise is essential. Dark subtraction is the most effective way to do that in narrow band, where there is no sky noise to hide the read and thermal noise. Dark subtraction fixes FPN, but introduces random noise (read and shot), so, to make quite sure that you get an advantage (ie the noise reduction from fixing FPN outweighs the increase in random noise), I think that it is probably prudent to have about 3x as many darks as lights. You will get OK results with less, but there will still be a little to gain by using as many as you possibly can. If you must use limited dark data, be sure to dither - that will help.
The other thing that you might try is to do a dark subtraction and then follow by a hot pixel substitution - this will get rid of shot noise associated with warm and hot pixels (which can show up as minor dark and bright spots even after dark subtraction).
If you use PI to calibrate, it will make optimum use of whatever dark data you have - even if it does not exactly match the light exposures.
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Thanks Ray, lots of good info there, as always :-)