Method and system for reconstructing super-resolution image
US10181092B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 6, 2017 |
| Grant date | Jan 15, 2019 |
| Priority date | — |
| Expiry date | Apr 6, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20224
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for reconstructing a super-resolution image, including: 1) reducing the resolution of an original high-resolution image to obtain an equal low-resolution image, respectively expressed as matrix forms yh and yl; 2) respectively conducting dictionary training on yl and yhl to obtain a low-resolution image dictionary Dl; 3) dividing the sparse representation coefficients αl and αhl into training sample coefficients αl_train and αhl_train and test sample coefficients αl_test and αhl_test; 4) constructing an L-layer deep learning network using a root-mean-square error as a cost function; 5) iteratively optimizing network parameters so as to minimize the cost function by using the low-resolution image sparse coefficient αl_train as the input of the deep learning network; 6) inputting the low-resolution image sparse coefficient αl_test as the test portion into the trained deep learning network in 5), outputting to obtain a predicted difference image sparse coefficient {circumflex over (α)}hl_test, computing an error between the {circumflex over (α)}hl_test.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.