I'm not familiar enough with the field to understand how the "neutral net" part feeds in, other than to do parallel computation on the x-pos, y-pos, (RGB) color-type-intensity tensor interpolated/weighted into a larger/finer tensor.
(linear algebra speak for upscaling my old DVD to HD, that sort of thing)
At the risk of exposing my ignorance, this has nothing to do with "AI", right? It's "just" parallel computation?
this may make you feel disappointed now, but in the write up we are also pitching this same module to be used in generative networks and other models that do build an understanding of the scene. Lets see what the community (and ourselves) can do next...
Approaches in the past used heuristics (like finding edges and upsampling them, etc). Those were fragile systems. In this approach, the system learns what's appropriate on its own.
Unless we are telling it to "be intelligent" whatever that means.
(from https://en.wikipedia.org/wiki/Artificial_intelligence)
I missed the part, though, where there was some "learning"/"adjusted predication" in the interpolation function(s), rather than just a fixed calculation such as a literal linear interpolation.
I was happy just to be able to tease apart the big equation before the the python code sample, but was too lazy to drill down into what the "delta-x"/"delta-y" factor-functions were.
Still, this was a good presentation: somebody with little to no knowledge of the field, but some math, could get the gist of it. Kudos to the author.