Real life CSI: Google's new AI system unscrambles pixelated faces
Real life CSI: Google's new AI system unscrambles
pixelated faces
Company reveals technology capable of increasing picture
resolution 16-fold, effectively restoring lost data – but results still an
educated guess
By Alex Hern Wednesday 8 February 2017 07.08 EST modified
Wednesday 8 February 2017 07.18 EST
Google’s neural networks have achieved the dream of CSI
viewers everywhere: the company has revealed a new AI system capable of
“enhancing” an eight-pixel square image, increasing the resolution 16-fold and
effectively restoring lost data.
The neural network could be used to increase the
resolution of blurred or pixelated faces, in a way previously thought
impossible; a similar system was demonstrated for enhancing images of bedrooms,
again creating a 32x32 pixel image from an 8x8 one.
Google’s researchers describe the neural network as
“hallucinating” the extra information. The system was trained by being shown
innumerable images of faces, so that it learns typical facial features. A
second portion of the system, meanwhile, focuses on comparing 8x8 pixel images
with all the possible 32x32 pixel images they could be shrunken versions of.
The two networks working in harmony effectively redraw
their best guess of what the original facial image would be. The system allows
for a huge improvement over old-fashioned methods of up-sampling: where an
older system might simply look at a block of red in the middle of a face, make
it 16 times bigger and blur the edges, Google’s system is capable of
recognising it is likely to be a pair of lips, and draw the image accordingly.
Of course, the system isn’t capable of magic. While it
can make educated guesses based on knowledge of what faces generally look like,
it sometimes won’t have enough information to redraw a face that is
recognisably the same person as the original image. And sometimes it just plain
screws up, creating inhuman monstrosities. Nontheless, the system works well
enough to fool people around 10% of the time, for images of faces.
Running the same system on pictures of bedrooms is even
better: test subjects were unable to correctly pick the original image almost
30% of the time. A score of 50% would indicate the system was creating images
indistinguishable from reality.
Although this system exists at the extreme end of image
manipulation, neural networks have also presented promising results for more
conventional compression purposes. In January, Google announced it would use a
machine learning-based approach to compress images on Google+ four-fold, saving
users bandwidth by limiting the amount of information that needs to be sent.
The system then makes the same sort of educated guesses about what information lies
“between” the pixels to increase the resolution of the final picture.
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