Approximating image processing functions using convolutional neural networks
US10430913B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 30, 2017 |
| Grant date | Oct 1, 2019 |
| Priority date | — |
| Expiry date | Mar 21, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are provided for approximating image processing functions using convolutional neural networks (CNNs). A methodology implementing the techniques according to an embodiment includes performing, by a CNN, a sequence of non-linear operations on an input image to generate an output image. The generated output image approximates the application of a targeted image processing operator to the input image. The CNN is trained on pairs of training input and output images, wherein the training output images are generated by application of the targeted image processing operator to the training input images. The CNN training process generates bias parameters and convolutional kernel parameters to be employed by the CNN for processing of intermediate image layers associated with processing stages between the input image and the output image, each of the processing stages associated with one of the sequence of non-linear operations. The parameters are associated with the targeted image processing operator.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.