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.