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gamesindustryblog.com May 29, 2017


New Google Brain research brings the 'zoom and enhance' trope to reality

09 February 2017, 12:42 | Muriel Sparks

Enlarge Image Google

Enlarge Image                  Google

With Google Brain's new project, however, reality is catching up to fiction. How will it do so? A demonstration of what the technology was able to do is seen in the photo above.

In the capture above, you have an unrecognizable low-resolution image on the left, the computer-enhanced image in the middle, and the real image on the right.

So how is Google Brain able to achieve this wizardry?

Because there is so little detail in the down sampled image, the system needs a powerful hint to get started. And also when performing photo searches on Google+. In essence, the system is taking an educated guess about what single pixels could mean and enhances them with additional ones. The algorithm has been run on faces (the left two columns) and bedrooms (the right two columns). It extracts an unbelievable amount of information from a low-detail source, basically. But how will it do so? It converts these more detailed images to 8 x 8 and determines if there's a match. This sort of approach fills in the blanks so to speak but if your zoomed-in source is only a couple pixels wide there's nothing you can do. The Google software takes this image and tries to extract information from it. But how can it do this? With the help of neural networks, of course; two, to be exact. The Google Brain software is, quite expectedly, powered by artificial intelligence. Like, say, an 8×8 image.

Some nerdy-looking "hacker" then clacks at his keyboard and - boom - seconds later, pixelated image turns into a crisp one revealing the person's face in glorious detail. This will already contain a large quantity of image data.

It will do so as it implements PixelCNN. The process will begin after the 8×8 is upscaled.

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They will be chosen so as to match to its image class. To put it simply, it will choose details so as to place human details where it should, on a portrait.

Finally, the images produced from both neural network training sessions are then composited together to create the best approximation of what the original image might be. Usually, this results in a plausible and probable result.

Google Brain's super-resolution technique was reasonably successful in real-world testing. When images of a bedroom were used, 28 percent of human subjects were fooled by the computed image. Which was compared to a high-res initial photo. The additional details are at best guesses and nothing more. The research paper is titled as follows.

The "hallucination" process involves first giving the software a tiny image to look at. An accurate picture is as yet unavailable.

Google Brain created a new software that makes pixelated images clear.



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