A lot of replies here which are based on out of date information and misunderstandings about deep learning super resolution (DLSR). Firstly, this isn't just do

A lot of replies here which are based on out of date information and misunderstandings about deep learning super resolution (DLSR). Firstly, this isn't just doing edge detection etc as happened 20 years ago. It's creating a deep learning model which fills in information based on extrapolating from looking at lots of other images and making an educated guess as to what is most likely to be present. It's a fairly new approach and works much better than previous methods, given enough training data. This is, of course, imperfect, but claims that it's just to "look good" and is of no practical benefit are incorrect. For instance, we published a paper using DLSR in microscopy, to help experts identify synaptic vesicles. In the original images the experts had a 3x higher false negative rate compared to the DLSR images. Finally, claims that this can't be called "super resolution" are ignoring years of peer reviewed published research in which this is exactly the name used for this approach. Yes, super resolution can also be achieved using other methods which take advantage of additional data (such as multiple images), but that does not mean DLSR has to be called something else. (I'm not involved in ArcGIS Pro, but am the lead author of the fastai framework which underpins ArcGIS training behind the scenes, and have published papers and tutorials on super resolution using deep learning.)