Hi Juan,
Yes, your 'dirty workaround' does work.
My Bias Frames, which originally have a nice Gaussian distribution curve, centred around a typical Mean of about 3970, with a StDev of about 40 and a Minimum of around 3780 and a Maximum of around 4170, now appear as they should when I open them - with the 'peak' way down at the bottom end of the [0,1] range.
Obviously, if I now apply the ReScale process, or if I use the Histo [ClipLow/ClipHigh] process, then I get what I was seeing before - the same very nice Gaussian distribution, but it is now more or less centred on 0.500, and with a Min/Max of 0.000 and 1.000 respectively. Absolutely NON-typical of a Bias frame - and therefore useless for further processing as such.
However, I still cannot use the ImageIntegration process - there is obviously an internal rescale() call that is being applied. I cannot tell whether you call this prior to processing each image, or whether you rescale the final integrated image after 'combining'. In any case, because the rescale call IS made, once again the resulting image has a (now 'very' nice) Gaussian curve - but back to being centred around 0.500
Am I missing some critical point here? Why can the image data not just be left 'as is' after the combination step?
NONE of the four possible combination methods (Average, Median, Maximum, Minimum) are mathemagically capable of generating an output image with 'out of range' values - providing the original images were all within range themselves. OK, sure, at SOME POINT during the 'Average Combine' process, the 'working image' might contain values that can be up to 'n-times' out of range - but as soon as that 'summed' image is then divided by 'n' again (to provide the 'averaged' result) the image MUST return back to 'in range' again.
I just don't see the need for a hard-coded rescale() call. You could, if needed, provide it as an option in exactly the same way as is done in PixelMath.
Which is a case in point - I can use PixMath to 'add' all the images together, and 'divide-by-n', and I get EXACTLY the result I am after (obviously without the Winsorized Clipping that I get from ImageIntegration). But, if I enable the ReScale option in PixMath, I lose the correct 'position' of the Gaussian curve - the Mean of the rescaled image ends up back at 0.500 again.
Don't get me wrong - there IS a possibility that I will NEED to have the image 'rescaled' for it to be useful as a calibration frame. I haven't got that far - my brain seems to be 'hung up' on these 'entry-level' issues.
But, I really do feel that - if you ARE automatically implementing a rescale() call - then I (for one) would like to be able to play with ImageIntegration with at least the OPTION of having the call being made, or not.
I hope I have been able to explain myself clearly - I think my brain is on the brink of shut-down. Perhaps I need to run a defrag on it. Tequila should do the trick
Cheers,
