In quality, yes, but they're only feasible for stuff like batch processing because they're so huge and slow. You can't use them for stuff like scaling an <img> in a browser.
Incidentally, the way I always thought it would work is the huge and slow AI would be used to discover a tiny algorithm that just so happens to be an amazing image rescaler. Instead we just ship the whole AI.
For scientifically correct scaling (think forensics) you will of course need to interpolate the source data instead of generating synthetic data which is done by AI upscaling.
AI algorithms encode domain knowledge in their weights that isn't present in simple interpolation functions. This can lead to better perceptual quality.
For lots of uses cases, if the end output meets user expectations, does it really matter if it was interpolated from simulated data?
For many use cases (editing old family photos etc), I'm increasingly not caring. I don't need per-pixel accuracy, I need a nice artifact that reminds me of a memory. If it looks 100 percent accurate to my eye, I'm not sure I care how much "fake" data it took to generate it. The important thing is it records the scene as I remember it.
This isn't hypothetical either; Photoshop already shipped their first iteration of the idea, for sure won't be the last:
Obviously if you don't care, it doesn't matter to you. But you not caring about objective reality versus your own vibes doesn't mean no one else has a valid reason to care. I personally would prefer my family photos not to contain machine generated hallucinations, because that doesn't actually represent reality.
But, just as obviously, you're correct that AI is going to force itself into every possible application and it doesn't matter.
No, but information isn't contained entirely in the image, but also in the assumptions about it. Simulation is exactly how you can retrieve more information than explicitly encoded in pixels - the information comes from how assumptions about the universe constrain possible states that would downscale to your original image.
I can take an photo of a locked door and no amount of assumptions you can make will allow you to retrieve generate an accurate photograph of what's behind it. You can assume anything. An empty room. A pile of money. Two unicorns copulating. Assumptions about an image are not information about the image, by definition.
You'd be surprised. Depending on the resolution of the photo, I might be able to get a low-res image of what's behind the door through the light leaks in its cracks. The more I know about the camera you used, the specific setting you took that photo in, the better chance I have at making a good guess of what's behind the door. Or behind the camera. These are the "assumptions" I'm talking about. External knowledge applied to resolve ambiguities.
For simpler examples, compare:
- GPS signal power at the receiver is under the thermal noise floor, yet they can still be recovered if you know exactly where to look.
- Nonlinear image transformations like blurs can still be inverted if you know or can guess the transform and exact coefficients used. Which is why, to redact something, you really want to paint over it with uniform color, instead of blurring it.
After a certain level of zoom, simulation should be expected. Interpolation will only take you so far. If someone wants to zoom in 1000x and see a microscopic image, not just a smear of pixels, then so be it.
If you only want an image to be 100% realistic you must stay at the original zoom level.
