> Except, and this is really the fundamental catch, it's not so much "enhance" as it is "project a believable substitute/interpretation".

I would argue that this is a form of enhancement though, and in some cases will be enough to completely reconstruct the original image. For example, if I give you a scanned PDF, and you know for a fact that it was size 12 black Ariel text on a white background, this can feasibly let you reconstruct the original image perfectly. The 'prior' that has been encoded by the model from the large amount of other images increases the mutual information between grainy image and high-res. The catch is that uncertainty cannot be removed entirely, and you need to know that the target image comes from roughly the same distribution as the training set. But knowing this gives you information that is not encoded in the pixels themselves, so you can't necessarily argue that some enhancement is impossible. For example with celebrity images, if the model is able to figure out who is in the picture, this massively decreases the set of plausible outputs.