There is a new movement right now in data augmentation I found it used in most of the super-resolution problem. for example these lines in “Residual Dense Network CVPR 2018”

we randomly extract 16 LR RGB patches with the size of 32 × 32 as inputs. We randomly augment
the patches by flipping horizontally or vertically and rotating 90◦ . 1,000 iterations of back-propagation constitute an epoch

which I guess either they are doing nested-looping or they are augmenting the dataset offline before training.
They said training on DIV2K they have 1000 iteration per epoch for this patche size while you should have 50. it means 20 times larger training per epoch.