Learning parameters for an image harmonization neural network to generate deep harmonized digital images
US12299844B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 13, 2024 |
| Grant date | May 13, 2025 |
| Priority date | — |
| Expiry date | Feb 13, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20212
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”). Additionally, the disclosed systems can utilize the self-supervised image harmonization neural network to generate harmonized digital images that depict content from one digital image having the appearance of another digital image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.