As the opposed means highlights algorithm now works fine for xtrans I am working on segmentation based recovery for xtrans.
What I need to verify the quality of the algo on xtrans are more images to test.
Come on fuji friends, if you want something really good we need your help!
We need fotos with large parts of the image blown out, like sky and clouds. In bright light and also classic sunset images. Or indoor with arteficial blue or red lights.
Perfect would be images with dark structures in the sky, window frames, power lines, orangeries…
Look out for badly exposed images in your collection and share!
Blown highlights on textured rock: DSCF8663.RAF (28.4 MB)
Here are 3 bracketed images that can be useful because the darker ones can give a ‘ground truth’ for the blown highlights: DSCF7510.RAF (22.7 MB) DSCF7508.RAF (22.9 MB) DSCF7509.RAF (24.6 MB)
I also have a full training set for my LUTs with 80 exposure series. I will make that image set available to you just as soon as it finishes uploading. It is a bit big (4.5 GB).
Here’s the aforementioned training set: 400 images (4.43 GB). Please let me know when you have downloaded the images, so I can free up the storage. It’s not a very large server.
The pr has just been updated with some fixes. A am not aware of remaining issues, might also have fixed windows build issues. My tests show equal quality compared to Bayer.
Fuji friends, could you spare some of your precious time and do some hard testing?
Of special interest are
Transitions from dark to clipped, do pixel peep here!
Before and after highlight reconstruction of some of the images. I am a RawTherapee user and I don’t have any experience with DarkTable. I was just curious to see the results.
Addendum: Jens-Hanno’s latest wizardry (segment-based)
is now added to the image suite above.
For that specific image, segment-based highlight recovery
seems a bit better than the inpaint opposite method.
(See, for instance, the darkblue contours around the white clouds
in the inpaint opposite method — but segment based is a bit fluffier.)