Quote:
Originally Posted by gvanhau
Hello Steven
What a big halo, never seen that before.
Are there any documents where I can read/study about the techniques involved to extract such info from (almost) ordinary images?
Geert
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Hi Geert.
I'm intending to write up a procedure based on Pixinsight in the not too distant future. Any software package however that allows data manipulation using pixel maths will do.
Here is a general overview.
For skyglow subtraction process the luminance image normally and make sure there is no clipping in the black region.
To subtract the skyglow one must accurately measure the noise in the background.
The noise value is used in a conditional function.
For example suppose the background noise value is n.
The conditional function is IF(PV<n, 0, PV) where PV is the pixel value being tested for the condition.
If PV<n, then PV is replaced by 0.
If PV=>n then PV is unchanged.
The key to success is the accurate measurement of the background noise.
The background is now black and largely noiseless and allows aggressive stretching without loss of contrast as the background remains black.
I have found the mapping function:
PV(new)= PV(old)*exp(-0.2*PV(old))
where PV(new) is the PV after mapping and PV(old) the value before mapping, to be superior to linear and non linear stretching that are typically used, as both the low and high pixel range remain largely unaffected while stretching is performed in the mid range.
When this non linear stretch is used after the skyglow is subtracted, one can extract very faint details in the image.
Regards
Steven