The raws are now posted on https://nc.trougnouf.com/index.php/s/5HH8okM6rQBnFAo however that’s extremely slow (it’s on my home internet since I currently don’t have a host with this much storage) and disorganized at the moment. I will get around to organizing these, probably when I redevelop them into the right format (16-bit tiff with less processing.)
Linear processing should indeed greatly simplify the required network, and will probably improve the results too. I’m still not entirely clear about how to properly output images pre black point subtraction. Is it the “passthrough” “raw/white black point” in darktable? This module is applied before demosaic, is that not problematic if we want to add “nn denoising” in the pipeline? Most importantly, would this produce significantly different results for each sensor and hurt generalization? Could you share a xmp file with the ideal amount of processing applied? I think that would be demosaic, matched white balance (I guess it would be best to leave it at reference / 6502K? that matches the new color calibration module too), and matched exposure (I used darktable’s auto exposure with a % value, because it varies between shots even though the camera EV setting remains the same, though that should be pretty easy to do with any other tool/library).
In my experience many noisy shots did not result in the same level of detail as one base-ISO shot, but maybe I did not experiment enough with this method.
I’ve attached a couple of processing candidates; minimal with/without(_01) raw black/white point passthrough, reference 6502K white balance, auto exposure set to match the baseline image, and Linear Rec. 2020 output color profile.
baseline (raw): DSCF7745.RAF (32.2 MB)
exported w/ passthrough raw black/white point: DSCF7745.RAF.xmp (16.5 KB) , DSCF7745.tif (58.3 MB)
exported with std raw black/white point: DSCF7745_01.RAF.xmp (16.5 KB) DSCF7745_01.tif (70.9 MB)
matching ISO6400 raw file: DSCF7748.RAF (32.2 MB)