Furthermore, the trained model does very well on images that are similar to image pairs that it was trained on and does not do as well on images that are very different.
The downside is that it requires a LOT of examples and a massive amount of computational power in order to properly train an AI algorithm to do this task well. Having been trained on a large volume of image sets containing high and low resolution pairs, it becomes capable of generalizing the process of upscaling similar images. This is where artificial intelligence comes into play.
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