Yep, this is more or less the same concept behind
EQClassicHD - originally it was just going to be a mostly automated upscaling project using
waifu2x, which uses algorithmic methods self-described as "single-image super-resolution for anime-style art using deep convolutional neural networks". For our project, we found that a combination of algorithmic enhancement with some human finessing produced the best results, but as AI upscaling technology and computer technology in general continues to advance I'm sure superior results will be achievable.
"Machine learning" might not be proper terminology for this (although technically it is a form of machine learning or "deep learning"), since machine learning is usually characterized by a self-sustained iterative process like that of DeepMind, which essentially teaches and improves upon itself with insane results like achieving the equivalent of several hundred years of human progress in the span of a week. This is how AlphaStar recently went undefeated in a professional StarCraft II league, and how AlphaGo became the world's reigning Go champion, and so forth. Essentially what they do is teach an AI neural network the fundamentals of the game by letting it observe videos of real people playing, and then have the AI play against itself in a series of tournaments to produce a dominant AI "agent", which they then pit against another agent that observed different videos and played against itself in an entirely separate series of tournaments, etc. Pretty fascinating stuff, and humanity is doomed I'm sure.
Algorithmic upscaling kinda seems more like machine learning in microcosm, and a lot of the same concepts apply - it's inferring the increased detail during the upscale process, and I suppose it is iterative in that newer and better algorithms are being produced.