Totally agreed. I take some issue with the verbiage, particularly:
> "Figure 1: Recovering high resolution image from low resolution"
This is terribly misleading. There is no data being "recovered" here. A ML model is guessing at the result based on other training data. It may (and in fact is likely to) make stuff up entirely based on what it thinks should be there.
I'm generally pretty live and let live when it comes to ML-based upscaling, because if some drawing or personal photograph has some artifacting it's pretty harmless. But when you're doing it in a tool whose data will be relied upon for Real Stuff, one needs to be painfully accurate when it comes to what the system does and its limitations.
> "Figure 1: Recovering high resolution image from low resolution"
This is terribly misleading. There is no data being "recovered" here. A ML model is guessing at the result based on other training data. It may (and in fact is likely to) make stuff up entirely based on what it thinks should be there.
I'm generally pretty live and let live when it comes to ML-based upscaling, because if some drawing or personal photograph has some artifacting it's pretty harmless. But when you're doing it in a tool whose data will be relied upon for Real Stuff, one needs to be painfully accurate when it comes to what the system does and its limitations.