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. Resear

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.