Let's Enhance: https://www.youtube.com/watch?v=Vxq9yj2pVWk

Literally: https://letsenhance.io.

These enhance effects typically use guesswork. That would be useful to gain hints, but not to know for sure what the original looked like.

Unless the rest of the image offers details that the human observer would ignore but the AI would pick up. E.g. low res photo from 1828 of a US president - enhance to John Quincy Adams.

Surprisingly there's a daguerreotype of John Quincy Adams from 1843[1], the AI could use that; perhaps "enhance" the time backwards a bit, to make him 15 years younger.

[1]: https://en.m.wikipedia.org/wiki/File:John_Quincy_Adams_-_cop...

Right, and that would be guesswork unless the image had been explicitly tagged.

There are search engines that search for similar images; a small photo of John Quincy Adams may be 99.99% identifiable as such.

An educated guess then, maybe even a good one!

What happens if it's only 80% sure? Do you get a high-res John Quincy Adams or an admission that it can't identify the image content? If I feed it a low-res picture of Michael Faraday or Henry Clay, will I receive the finely sculpted jowls of John Quincy Adams in return?

Guesswork, deduction, tomayto, tomahto ;)

Isn't that what Samsung was doing with photos of the moon?

https://www.theverge.com/2023/3/13/23637401/samsung-fake-moo...

But I also had a similar thought. Use a combination of an image search engine like TinEye, and something like ClearviewAI, to actually either find a larger image of the submitted photo, or of more photos of the person in question, and reconstruct what they look like based on all of that additional data.

Yes. But I'd like to take it one step further.

E.g. if I send a photo of an olympic podium where winners are pixelated and ask the AI to enhance, I'd like it to use other elements of the pixelated photo to recognize the olympic event and year, search some other database to find the winners and enhance the photo so that the people on the podium were those who actually stood on that podium.

The one exception being video-based image super-resolution techniques, where the frames of the video are composited together to gain more information than is in any individual frame (which is also how MRIs work!)

Tangent to that: does anyone know if there are any free-to-use tools (or open-source models) for doing video-based image super-resolution?

(I imagine that such a tool could take video files directly; but that the actual underlying model would likely require that you first convert your video into an image containing a grid of sampled frames, making it essentially an image2image model that learns to sample from reference images. A bit like the image2image models trained to output depth maps given stereo-camera photo pairs.)

I’m going to train an AI in Visual Basic, see if we can spot the killer

You forgot to zoom first.