[dakial1]

Some of the images in the article seemed to be high-res images that where downscaled to low-res (and it makes sense to see how the upscalling process changes the original), but wouldn't that make it easier for the ML to revert the downscaling process rather than taking an original low-res photo and upscale it?

[lwneal]

This is true. Downscaling an image and then training a neural network to scale it back up is the way single-image superresolution systems typically work. Research papers need to evaluate their models, and how can you evaluate a scaled-up image unless you have the original ground truth to compare it to?

This can introduce a dataset shift bias. For example, if you train a network to upscale 1080p movie frames to 4k, the results might be disappointing when you try to scale 4k to 8k.