Question to anyone that knows this area in depth.

I'm assuming that this impressive feat has a disadvantage (over the traditional example intensive technique of training a CNN with something like ImageNet) in the form of taking a long time to generate the corrected image.

With that assumption in mind, could this new technique be reversed? As in feeding in sharp images, getting back out corrupted ones, for the purposes of generating data sets where there isn't much data to begin with?

You could then take that data and use it to train a more traditional CNN to sort of amortize the results of the technique in the paper and have the process happening faster.