Google’s neural networks turn pixelated faces back into real ones by John E. Dunn.
From the post:
Researchers at Google Brain have come up with a way to turn heavily pixelated images of human faces into something that bears a usable resemblance to the original subject.
In a new paper, the company’s researchers describe using neural networks put to work at two different ends of what should, on the face of it, be an incredibly difficult problem to solve: how to resolve a blocky 8 x 8 pixel images of faces or indoor scenes containing almost no information?
It’s something scientists in the field of super resolution (SR) have been working on for years, using techniques such as de-blurring and interpolation that are often not successful for this type of image. As the researchers put it:
When some details do not exist in the source image, the challenge lies not only in “deblurring” an image, but also in generating new image details that appear plausible to a human observer.
Their method involves getting the first “conditioning” neural network to resize 32 x 32 pixel images down to 8 x 8 pixels to see if that process can find a point at which they start to match the test image.
…
John raises a practical objection:
…
The obvious practical application of this would be enhancing blurry CCTV images of suspects. But getting to grips with real faces at awkward angles depends on numerous small details. Emphasise the wrong ones and police could end up looking for the wrong person.
…
True but John presumes the “suspects” are unknown. That’s true for the typical convenience store robbery on the 10 PM news but not so for “suspects” under intentional surveillance.
In those cases, multiple ground truth images from a variety of angles are likely to be available.