Journal article
Dual-plane wavefront sensing using a vision transformer
Evan O'Rourke,
Kevin O'Keeffe 
Optics Express, Volume: 34, Issue: 4, Pages: 6455 - 6467
Swansea University Authors:
Evan O'Rourke, Kevin O'Keeffe
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1364/oe.586748
Abstract
Image-based wavefront sensing using deep-learning allows Zernike coefficients to be estimated directly from intensity measurements. To date, the majority of experiments have focused on using convolutional neural networks to estimate coefficients. Here we demonstrate a dual-plane wavefront sensor tra...
| Published in: | Optics Express |
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| ISSN: | 1094-4087 |
| Published: |
Optica Publishing Group
2026
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71410 |
| Abstract: |
Image-based wavefront sensing using deep-learning allows Zernike coefficients to be estimated directly from intensity measurements. To date, the majority of experiments have focused on using convolutional neural networks to estimate coefficients. Here we demonstrate a dual-plane wavefront sensor trained using a vision transformer model and compare its performance to that of the widely used convolutional neural network (CNN) architecture. Both results of experiment and simulation indicate that the vision transform can outperform the CNN where image data is significantly down sampled, due to the former's ability to more accurately predict high-order Zernike coefficients. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
Engineering and Physical Sciences Research Council |
| Issue: |
4 |
| Start Page: |
6455 |
| End Page: |
6467 |