Training networks without wavefront label for pixel-based wavefront sensing
Traditional image-based wavefront sensing often faces challenges in efficiency and stagnation. Deep learning methods, when properly trained, offer superior robustness and performance. However, obtaining sufficient real labeled data remains a significant challenge. Existing self-supervised methods ba...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1537756/full |
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| Summary: | Traditional image-based wavefront sensing often faces challenges in efficiency and stagnation. Deep learning methods, when properly trained, offer superior robustness and performance. However, obtaining sufficient real labeled data remains a significant challenge. Existing self-supervised methods based on Zernike coefficients struggle to resolve high-frequency phase components. To solve this problem, this paper proposes a pixel-based self-supervised learning method for deep learning wavefront sensing. This method predicts the wavefront aberration in pixel dimensions and preserves more high-frequency information while ensuring phase continuity by adding phase constraints. Experiments show that the network can accurately predict the wavefront aberration on a real dataset, with a root mean square error of 0.017λ. resulting in a higher detection accuracy compared with the method of predicting the aberration with Zernike coefficients. This work contributes to the application of deep learning to high-precision image-based wavefront sensing in practical conditions. |
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| ISSN: | 2296-424X |