Similar to what is used in the iconic 'Blade Runner' movie, the EnhanceNet uses neural networks to turn low resolution images into high resolution ones.
They wrote in their report: "Enhancing and recovering a high-resolution (HR) image from a low-resolution (LR) counterpart is a theme both of science fiction movies and of the scientific literature. In the latter, it is known as single image super-resolution (SISR), a topic that has enjoyed much attention and progress in recent years.
"The problem is inherently ill-posed as no unique solution exists: when downsampled, a large number of different HR images can give rise to the same LR image. For high magnification ratios, this one-to-many mapping problem becomes worse, rendering SISR a highly intricate problem. Despite considerable progress in both reconstruction accuracy and speed of SISR, current state-of-the-art methods are still far from image enhancers like the one operated by Harrison Ford alias Rick Deckard in the iconic Blade Runner movie from 1982."
This AI is able to produce "sharp results for natural images" quickly and efficiently.
They added: "In this work we pursue a different strategy to improve the perceptual quality of SISR results. Using a fully convolutional neural network architecture, we propose a novel modification of recent texture synthesis networks in combination with adversarial training and perceptual losses to produce realistic textures at large magnification ratios.
"The method works on all RGB channels simultaneously and produces sharp results for natural images at a competitive speed. Trained with suitable combinations of losses, we reach state-of-the-art results both in terms of PSNR and using perceptual metrics."