How did Shiryaev do it? He says he used commercial image-editing software called Gigapixel AI. Created by Topaz Labs, the package allows customers to upscale images by up to 600 percent. Using sophisticated neural networks, Gigapixel AI adds realistic details into an image to avoid making it look blurry as it's scaled up.
As the name implies, neural networks are networks of artificial neurons—mathematical functions that transform a set of input values into an output value. The key feature of neural networks is that they can be trained: if you have a bunch of example inputs whose "correct" outputs are known, you can tune the parameters of the network to make it more likely to produce correct answers. The hope is that this training will generalize—that once you've trained it to produce the right answer for inputs the network has seen before, it will produce good answers for inputs it hasn't seen, too.
To train a network, you need to have a database of examples where the right answer is already known.Sometimes AI researchers have to hire human beings to produce these right answers by hand. But for image upscaling, there's a convenient shortcut: you start with high-resolution images and downsample them. The low-resolution images become your inputs, and the high-resolution originals serve as the "correct" answer the network is aiming to produce.
