I'm sceptical, but it's nice work if you can get it.
I presume the idea is for the system to (implicitly) identify contiguous regions in the image and 'patch over' them with smoothly interpolated colour. I'm sure it works perfectly - until it doesn't..!
I speak as someone who once became obsessed with neural networks as the answer to everything, but gradually began to understand that although they miraculously seem to obviate the need for complex mathematics and deep understanding, they are really just transferring it to the problem of analysing and selecting the right training data!
Many of these AI systems work at 90% effectiveness on the very first day you try them with a smattering of data. It is very easy to 'sell' the project at this stage. (To yourself and other people).
Three years later and running a massive server farm of training and testing data, you're at 80%. But the system will be useless unless you get to 99.99%. How long can you spin the project out for..? At first you thought you would be happy to do it forever, but you'd really much rather go off and do something else..!
Edit:
Just as an example. Suppose you decided that a common problem in smartphone photographs was 'chopping off feet'. Could AI help here?
Well, you could indeed show some neural networks thousands of pictures with chopped off feet against the desired image, having created these examples from real photographs. The networks would 'learn' what was expected, possibly learning to implicitly identify legs and to extend them to an average length relative to head size (that it also identified) and to place on the end of them some commonly recurring blobs that it saw in the examples.
But would it substitute the right sort of shoe? If you only showed it training shoes it would only know about training shoes. If you showed it many types of shoes, it
might learn to associate women with high heels and men with trainers, but on the other hand it might find that its error is least if it substitutes a fuzzy composite average of all shoe types. Would it show the shoe in the correct pose? Again, if you don't get the balance of examples right they might end up as a fuzzy blob or pointing in the wrong direction. This is where an attempt is being made to substitute for understanding of human anatomy and analysis of perspective with dumb network training. The problem has simply been transferred to the selection of training data. Perversely, the scientist might start analysing the training data with algorithms before submitting them to the neural net training system! Or, failing that it is a long hard 'ad hoc' slog towards ultimate failure three years later...
The above example is 'obviously' doomed to failure, but many other examples may be equally doomed without it being obvious. If you are using neural networks in this way, it shows that you don't really understand the problem so cannot predict whether it is solvable or not.
Another dimension is the 'hilarious' results that might occur - and the problem of the Twittermob turning on you if your system ends up with inadvertent racial, gay or transexual prejudices built in!