Hmmm. With respect to feeding an ML system, are visual glitches and artifacts important? Wouldn't the most important thing to use a transformation which preserves as much information as possible and captures relevant structure? If the intermediate picture doesn't look great, who cares if the result is good.
Ooops. Just thought about generative systems. Nevermind.
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Hmmm. With respect to feeding an ML system, are visual glitches and artifacts important? Wouldn't the most important thing to use a transformation which preserves as much information as possible and captures relevant structure? If the intermediate picture doesn't look great, who cares if the result is good.
Ooops. Just thought about generative systems. Nevermind.
Just speaking from experience, GAN upscalers pick up artifacts in the training dataset like a bloodhound.
You can use this to your advantage by purposely introducing them into the lowres inputs so they will be removed.