The first component of the process is Super-Resolution via Repeated Refinements ( SR3), ‘a super-resolution diffusion model that takes as input a low-resolution image, and builds a corresponding high-resolution image from pure noise.’ In essence, this model applies pure Gaussian noise to a low-resolution image before using noise-reduction technologies to effectively reconstruct a nearly noise-less image that’s four times the resolution of the input. The before and after of increasing the resolution of the portraits from 64 x 64 pixels to 1024 x 1024 pixels.
The blog post, titled ‘ High Fidelity Image Generation Using Diffusion Models,’ explains how Google researchers have developed a pair of AI technologies that can take a low-resolution image and steadily increase resolution through selective destruction and reconstruction of the original input image. Or is it? A new blog post on the Google AI Blog showcases a new technology its developed to upscale low-resolution images with incredible results. Thanks to CSI, as well as plenty of other crime dramas, the phrase ‘Enhance’ has taken on a life of its own as a tongue-in-cheek way of attempting to digitally extract extra information from low-resolution images that simply isn’t feasible in real-world situations.
Cascaded generation of unconditional 1024x1024 faces.
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