State of the art in upsizing algorithms?

Started Jun 9, 2014 | Discussions thread
The_Suede Contributing Member • Posts: 652
Re: State of the art in upsizing algorithms?

The best you find at the moment are a priori trained superresolution engines. That's basically a neural network that tries to identify objects and shapes that it "knows about" since earlier, and then fills in the voids.

It's used a lot in aerial surveillance, it's also used to enhance the staples of ground surveillance - i.e faces and numberplates.

That has to be seen as a known unknown though, since your result will never be better than the training set of the neural.

Using classical interpolation schemes for Bayer images quite quickly falls apart due to noise effects. It's not like in weather forecasting, where a change of a hundredth of a percent in one single variable can make the difference between calm and a storm - but actually the metaphor isn't that far off... :/

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