(Author here.) My biggest insight from this project is that super-resolution with neural networks benefits significantly from being domain specific. If you train on broader datasets, it does pretty well but has to make compromises. Many recent papers do a comparison in terms of pixel similarity (PSNR/SSIM), and using those metrics the quality drops because high-frequency detail is punished under those criteria (even though it may look better perceptually). Reference: http://arxiv.org/abs/1609.04802

On GitHub, below each GIF there's a demo comparison, but on the site you can also submit your own to try it out (click on title or restart button). Takes about 60s currently; running on CPU as GPUs are busy training ;-)