De-noising images using machine learning
US10311552B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 22, 2017 |
| Grant date | Jun 4, 2019 |
| Priority date | — |
| Expiry date | Jan 17, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to using a neural network to efficiently denoise images that were generated by a ray tracer. The neural network can be trained using noisy images generated with noisy samples and corresponding denoised or high-sampled images (e.g., many random samples). An input feature to the neural network can include color from pixels of an image. Other input features to the neural network, which would not be known in normal image processing, can include shading normal, depth, albedo, and other characteristics available from a computer-generated scene. After the neural network is trained, a noisy image that the neural network has not seen before can have noise removed without needing manual intervention.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.