You could, however, probably tile the downscale "rainbow table" in a way that would let you predict some degree of novel original from a sufficient number of tile samples.
Thing about downscale blur is that it's nearest-neighborish, so can be addressed with divide+conquer as blur effects stay local. You'd end up with a fairly large combination of potential tiles. Some wouldn't be viable faces, but we have classifiers for that already.
Entire combination trees can be culled that way to make the problem radically smaller, as long as you know it's supposed to be a face, so I don't know how hard it would really be. It's possibly pretty easy to come up with the N possible original faces with enough certainty to then match with potential targets of interest and make N small enough to use.
I only made it through the abstract but it looks like they’re matching the entire given LR image to a known entire HR image.
I’m saying with enough data you could potentially create a more predictive “magic sharpening” algorithm that didn’t strive to match a known original picture, but instead used that matching on divide & conquer subtiles of the original LR image against of a rainbow table of reduced HR tiles to predict a set of plausible HR images.
Basically if you can figure out with whatever context you have that the 4x4 brown smudge is very likely a brown cat, you can replace it with a brown cat. And if you know that, the orange/white/black smudge next to it is probably a calico, so stitch it in.
Of course the source image would have to be bigger than this, so it couldn’t be CSI-enhance icon to landscape, really more like a really good AI upscaler. You’d need a strong way to identify plausible scenes too. We can generate novel faces now, think this fuses the two concepts.
Thing about downscale blur is that it's nearest-neighborish, so can be addressed with divide+conquer as blur effects stay local. You'd end up with a fairly large combination of potential tiles. Some wouldn't be viable faces, but we have classifiers for that already.
Entire combination trees can be culled that way to make the problem radically smaller, as long as you know it's supposed to be a face, so I don't know how hard it would really be. It's possibly pretty easy to come up with the N possible original faces with enough certainty to then match with potential targets of interest and make N small enough to use.