Author here -- Generally in single image super-resolution, we want to learn a prior over natural high-resolution images, and for that a large and diverse training set is beneficial. Your suggestion sounds interesting, though it's more reminiscent of multi image super-resolution, where additional images contribute additional information, that has to be registered appropriately.
That said, our approach is actually trained on a (by modern standards) rather small dataset, consisting only of 800 images. :)
That said, our approach is actually trained on a (by modern standards) rather small dataset, consisting only of 800 images. :)