Digital image completion using deep learning
US10614557B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 16, 2017 |
| Grant date | Apr 7, 2020 |
| Priority date | — |
| Expiry date | Jan 3, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20104
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Digital image completion using deep learning is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a framework that combines generative and discriminative neural networks based on learning architecture of the generative adversarial networks. From the holey digital image, the generative neural network generates a filled digital image having hole-filling content in place of holes. The discriminative neural networks detect whether the filled digital image and the hole-filling digital content correspond to or include computer-generated content or are photo-realistic. The generating and detecting are iteratively continued until the discriminative neural networks fail to detect computer-generated content for the filled digital image and hole-filling content or until detection surpasses a threshold difficulty. Responsive to this, the image completer outputs the filled digital image with hole-filling content in place of the holey digital image's holes.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.