[PeterisP]

Well, it doesn't create any information that wasn't in the original data (nothing can do that, you can only lose information in processing) so if e.g. the characters can be recognized in the processed image of a licence plate, then by definition they could have been recognized from the original data as well in some manner.

However, they can make things more easily interpretable by humans. A rough analogy is turning up the contrast - given a very dark image of licence plate where the black parts are totally black (#000000) and white parts are just very dark (#010101), the characters definitely can be recognized even while human in normal conditions would just see it as totally black, and processing would help.

[placebo]

> Well, it doesn't create any information that wasn't in the original data (nothing can do that, you can only lose information in processing)

I'm not sure this is correct. In a sense, it does contain information that wasn't in the original inputs - i.e information added by the weights in the neural network which itself was obtained by information extracted from an enormous amount of previous samples. Of course, the largest and best trained neural network won't be able to tell the license number given 2 pixels of information, but I am curious as to the theoretical limits of what can be achieved in extreme cases of with very little information as input and a neural network that has almost limitless resources.