Hacker News Reader

Comment by cubefox | Hacker News Reader

Yeah, but surely ML models would at least work better than analytic algorithms. After all, when looking at a noisy picture, our brain is pretty good at distinguishing detail from noise, so it's not clear to me why an ML model couldn't have denoising performance similar to the human brain, even if it doesn't match the "cheating" denoisers used in ray tracing.

View on HN

It would probably help you to compare what you can do on a phone vs what you can do with desktop software (Lightroom/Photoshop, DxO, Topaz, CaptureOne, etc). It's generally quite good, with the exception of challenging liminal areas (e.g. hair, foliage).

Fwiw, Topaz -- which I have a license for but essentially never use -- has pretty incredible denoising & upsizing features (for both photo & video), but to get the optimal quality output you offload the processing to their cloud infra (and buy credits from them to pay for it). It's roughly the equivalent of a SWE using a local LLM that's good enough" vs a frontier model that's SOTA but requires a consumption-based subscription.

Interesting, so it seems to be an issue with heavy compute or RAM requirements.

I've got a 61mp camera, and an RX 7900XT. It takes about 15s/picture for DXO to denoise, which is a lot longer than people are willing to wait on a phone to take a photo. Topaz is even slower. A cloud service could be used to do it in post, but someone has to pay for that.

Yet in modern computer games, modern graphics cards denoise a scene in real-time at 60 frames per second using machine learning models [1][2] while doing all the other rendering at the same time. Granted, that's ray tracing, and the resolution is lower, and they technically cheat by using additional information, but it might be that DXO is not optimized very well.

1: https://blogs.nvidia.com/blog/ai-decoded-ray-reconstruction/

2: https://gpuopen.com/amd-fsr-rayregeneration/

Games will typically un at 4k or less which is about 8mp on the other hand it’s difficult to buy a stills camera with less than 20 mp, and >40mp is common. Most algorithms are n^2 in graphics as well so we wouldn’t expect a linear speed up. I’ve tried dxo, Lightroom, and topaz they all perform about the same so I don’t think it’s particularly unoptimized.

Would you be happy if ML detects that you are trying to capture the moon and replaced your shot with a memorized picture of the moon with match ing size and orientation ?

Autoencoders can presumably do this except that it would be operating at levels of patches rather than the entire Moon.

No would be unhappy :(

ML models on some Samsung phones did exactly that.

https://news.ycombinator.com/item?id=35107601

It was probably a result of an explicit company charter but such things can happen with modern ML models quite unintentionally.

> Yeah, but surely ML models would at least work better than analytic algorithms.

Bear in mind, a modern flagship phone doesn't just need to take photos - it also needs to record 4k video at 60fps.

Can't go too hard with the ML when you've only got 1/60th of a second to do it.

Reply on news.ycombinator.com