Generating super-resolution images using neural networks
US11869170B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 18, 2019 |
| Grant date | Jan 9, 2024 |
| Priority date | — |
| Expiry date | Sep 5, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes receiving a training image and a ground truth super-resolution image; processing a first training network input comprising the training image using the neural network to generate a first training super-resolution image; processing a first critic input generated from (i) the training image and (ii) the ground truth super-resolution image using a critic neural network to map the first critic input to a latent representation; processing a second critic input generated from (i) the training image and (ii) the first training super-resolution image using the critic neural network to map the second critic input to a latent representation; determining a gradient of a generator loss function that measures a distance between the latent representations of the critic inputs; and determining an update to the parameters.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.