| How does an upscaler in your TV work? How does it create information out of nothing? Imagine that you are learning a deep neural network on a huge amount of movies. You have access to all of the lovely Hollywood movies. You downscale them to a 480p resolution, and then try to learn a deep neural network to upscale the thing, maybe upscaling only 16x16 blocks of the image. It works amazingly well, and looks like magic. Maybe there was no visibility of pores on the face in the 480p downscale, but your model can learn to reproduce them faithfully. Sony has access to billions of movie frames in extremely large resolutions. Their engineers are definitely using this large amount of information to create statistical filters which upscale your non-hd, or maybe your HD to 4k HD. These filters work better than deterministic methods in this article. Why? Because the filters know much more about the distribution of the source (distribution of values of each individual pixel). They have exact information that one instead tries to assume (author in the article assumed that something in the source - be it noise or something else - behaves according the to Gaussian). If you know how to find the proper distribution, instead of assuming it, you can move closer to the information theoretical limits. Just imagine how fast these filters can be if you put them on an FPGA, it also explains why TV sometimes cost more than $2k. If you knew that your images would only contain car registration plates, you could definitely learn a filter that would be very precise in reconstructing the image when zoomed, you'd now find CSI zooming a little bit more realistic :D |
Yes, your result would be a very clear image of one possible license plate. An algorithm may be able to do slightly better than a squinting human, but ultimately you can't retrieve destroyed information.