Duke University researchers have developed an AI tool that can turn blurry, unrecognizable
pictures of people’s faces into eerily convincing computer-generated portraits, in finer detail
than ever before.
Previous methods can scale an image of a face up to eight times its original resolution.
But the Duke team has come up with a way to take a handful of pixels and create realistic-looking
faces with up to 64 times the resolution, ‘imagining’ features such as fine lines,
eyelashes and stubble that weren’t there in the first place.
The researchers say that the system cannot be used to identify people,: It won’t turn
an out-of-focus, unrecognizable photo from a security camera into a crystal clear image
of a real person.
Rather, it is capable of generating new faces that don’t exist, but look plausibly real.
While the researchers focused on faces as a proof of concept, the same technique could
in theory take low-res shots of almost anything and create sharp, realistic-looking pictures,
with applications ranging from medicine and microscopy to astronomy and satellite imagery.
The researchers will present their method, called PULSE, at the 2020 Conference on Computer
Vision and Pattern Recognition (CVPR), held virtually from June 14 to June 19.
Traditional approaches take a low-resolution image and ‘guess’ what extra pixels are
needed by trying to get them to match, on average, with corresponding pixels in high-resolution
images the computer has seen before.
As a result of this averaging, textured areas in hair and skin that might not line up perfectly
from one pixel to the next end up looking fuzzy and indistinct.
The Duke team came up with a different approach.
Instead of taking a low-resolution image and slowly adding new detail, the system scours
AI-generated examples of high-resolution faces, searching for ones that look as much as possible
like the input image when shrunk down to the same size.
The team used a tool in machine learning called a “generative adversarial network,” or
GAN, which are two neural networks trained on the same data set of photos.
One network comes up with AI-created human faces that mimic the ones it was trained on,
while the other takes this output and decides if it is convincing enough to be mistaken
for the real thing.
The first network gets better and better with experience, until the second network can’t
tell the difference.
PULSE can create realistic-looking images from noisy, poor-quality input that other
methods can’t.
From a single blurred image of a face it can spit out any number of uncannily lifelike
possibilities, each of which looks subtly like a different person.
The system can convert a 16x16-pixel image of a face to 1024 x 1024 pixels in a few seconds,
adding more than a million pixels, akin to HD resolution.
Details such as pores, wrinkles, and wisps of hair that are imperceptible in the low-res
photos become crisp and clear in the computer-generated versions.
The researchers asked 40 people to rate 1,440 images generated via PULSE and five other
scaling methods on a scale of one to five, and PULSE did the best, scoring almost as
high as high-quality photos of actual people.