> Also don't see any comparison with original high-res image so that we can see how true to life the generated images look?

Was wondering about that too. It certainly produces realistic-looking high-res images, but especially when the article talks about potential uses ranging "from restoring old family photos to improving medical imaging", it seems like accuracy may be more valued than "looking realistic".

> improving medical imaging

Spot on. This is not "zoom, enhance" — it is "fabricate plausible detail based on a training set". Using it for anything other than making pictures look nice would be disastrous.

Other people in these comments are talking about using it for law enforcement. Train it on a bunch of pictures of black people holding guns and now suddenly it will "reveal" guns in the hands of all black people in blurry CCTV footage. (This specific example is likely a little simplistic to be a problem in reality, but it demonstrates the problem of thinking it actually reveals some hidden detail.)

There are a _couple_ of originals here to compare: https://iterative-refinement.github.io/ I would like to see more though!

My understanding is that they present people with two images, the real original high-res image, and the upscaled image, and ask "Which of these is the real image". The extent to which people are confused demonstrates how good the algorithm is. If it was perfect, and there was no difference between the original and upscaled then you'd expect people to pick each image about 50% of the time randomly.

So many of these upscaling technologies feel like the sketch of crime scenes. Artistic, to be sure, but probably not as good for actually filling in details as we'd like. And downright dangerous if they set unreasonably high expectations of fidelity.