Harmonizing composite images using deep learning
US10867416B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 10, 2017 |
| Grant date | Dec 15, 2020 |
| Priority date | — |
| Expiry date | Mar 10, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20221
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems are provided for generating harmonized images for input composite images. A neural network system can be trained, where the training includes training a neural network that generates harmonized images for input composite images. This training is performed based on a comparison of a training harmonized image and a reference image, where the reference image is modified to generate a training input composite image used to generate the training harmonized image. In addition, a mask of a region can be input to limit the area of the input image that is to be modified. Such a trained neural network system can be used to input a composite image and mask pair for which the trained system will output a harmonized image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.