and yes, the paper in question here is not “AI” in that sense, it hasn’t seen anything other than the stack of images to use for denoising (no strong prior from other images of the same subject, say).
I use DxO Photolab with Deep Prime noise reduction and can attest to how well it works. I have one particularly challenging example where I took a photo with a Canon APS-C camera at ISO 6400 that was still underexposed by almost two stops. Normal noise reduction in ART made it usable, but just barely, whereas with DxO I never would have guessed it was taken in such low light. Here are the two versions:
In my experience, it does a great job of reconstructing the detail that is obscured by noise without creating artifacts. Yes, an extreme example like above will still have a bit of a waxy look to it (because additional detail was simply not present in the RAW file), but it still looks much better than what I could do with traditional methods.
I prefer a native Linux RAW editor in general, but I still fire up the ol’ Windows VM with Photolab for high ISO shots and use it basically as a preprocessor for ART. But that’s tedious.
You can use this (as mentioned by @hanatos above) relatively easily from within ART if you like, by writing a proper user command. Despite all the disclaimers and to-dos, I found it to be pretty amazing. The major downside is that it’s slow, especially if you don’t have a Nvidia GPU and run it on a mobile CPU like myself. But for the few times I needed it, it’s worth the wait imho. And dxo is not exactly fast either as far as I understood.
Very interesting! I’ll definitely give that a try. And yes, DxO Deep Prime is very slow, especially on a VM, but it only does the work on export, so I just walk away and do something else for a while.
I gave it a try with the images I posted above. First I did some processing on the original RAW image in ART, but no noise reduction or sharpening, and exported as 16-bit TIF. Then I ran the nind-denoise script on it. It’s not bad but also not nearly as good as DxO. I think I actually prefer the version done in ART with regular noise reduction (posted above) because of the artifacts this has produced on the shadow areas of the face. Maybe there are settings to tweak that could improve it.
One issue with the approach is that it’s very hard to get pictures of people (and even more so of birds & animals) at different ISO, since they move. But the denoiser needs to be really good with people since we’re much better at seeing artifacts in them.
One solution could be to first train a network that can add noise to images (which presumably should be easier) so that we can generate training samples from single low-ISO shots.
Would you be able to share the raw? I have a different experience, if anything the results I get are too clean / “waxy” for my taste. Here are a couple of examples from playraws:
Thanks for the tips. I used your approach except that I worked with the full sized image. After denoising, I brought it back into ART for final adjustments, exported, and only then resized. The denoising process is slow, but I think it’s faster than DxO on the VM. This version is definitely better than my first attempt. Still maybe not at DxO’s level, but impressive nonetheless. I’ll have to experiment with less-extreme examples and see how I like it compared to regular noise reduction.
good to hear. However, note that I wrote “Rec2020”, not “Linear Rec2020”. Rec2020 has a gamma of ~2.2, and that makes a lot of difference. I write this because I noticed that the jpg you attached has a linear rec2020 profile (which really is not suitable for 8-bit files btw…).
Yeah, I normally do too. The screenshot above shows the options I get when I click the Output Profile dropdown. I don’t see Rec2020 as an option. Going to check RawTherapee and see if that has it, since it’s in the documentation @afre referenced.
That’s ART, not RawTherapee? And what version? I’m still on 1.15. I do see one commit in the ChangeLog related to output color profiles, so maybe that’s what I’m missing. I’ll try to update tomorrow and see if it appears. Otherwise maybe @agriggio can help me figure out what I’m missing.