The “enhance” feature of sci-fi and police procedurals could soon become reality. Researchers from Google’s Brain Team have recently published an article titled “High Fidelity Image Generation Using Diffusion Models” on their Google AI Blog. With it, they claim to upscale images 4 to 8 times using image noise as a foundation to train neural networks. Let’s take a look at what the age of the geek has in store for us! The Google AI Blog article, published by Research Scientist Jonathan Ho and Software Engineer Chitwan Saharia goes in-depth on their approach. While this concept was first introduced in 2015, it was shelved to give other ideas a chance to flourish. But like the famous tortoise from children’s books, slow and steady might have won the race. Conceptually, it’s no different from the superscale feature in DaVinci Resolve or Nvidia’s DLSS resolution enhancement. But what’s under the hood is a completely different story. While still in early development, this new AI photo upscaling approach can lead to incredible resources. Not only for filmmakers but photographers and game developers alike. Talk nerdy to me Let’s break down what the Brain Team has done in detail. Jonathan and Chitwan’s approach combined different training methods to work their magic. Initially, they used something called Super-Resolution via Repeated Refinement (SR3), a super-resolution diffusion model that builds high-resolution images from low-resolution inputs by using pure noise. This model is trained using an image corruption process. AI Photo Upscaling From Noise – Before. Image Credit:...
Published By: CineD - Thursday, 2 September, 2021