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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1537756/full |
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| author | Yuxuan Liu Yuxuan Liu Yuxuan Liu Xiaoquan Bai Xiaoquan Bai Boqian Xu Boqian Xu Chunyue Zhang Chunyue Zhang Yan Gao Yan Gao Shuyan Xu Shuyan Xu Guohao Ju Guohao Ju |
| author_facet | Yuxuan Liu Yuxuan Liu Yuxuan Liu Xiaoquan Bai Xiaoquan Bai Boqian Xu Boqian Xu Chunyue Zhang Chunyue Zhang Yan Gao Yan Gao Shuyan Xu Shuyan Xu Guohao Ju Guohao Ju |
| author_sort | Yuxuan Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3747e64b58f649e7b898f3e428bdab46 |
| institution | DOAJ |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-3747e64b58f649e7b898f3e428bdab462025-08-20T03:00:59ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-03-011310.3389/fphy.2025.15377561537756Training networks without wavefront label for pixel-based wavefront sensingYuxuan Liu0Yuxuan Liu1Yuxuan Liu2Xiaoquan Bai3Xiaoquan Bai4Boqian Xu5Boqian Xu6Chunyue Zhang7Chunyue Zhang8Yan Gao9Yan Gao10Shuyan Xu11Shuyan Xu12Guohao Ju13Guohao Ju14Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaChinese Academy of Sciences Key Laboratory of On-Orbit Manufacturing and Integration for Space Optics System, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaChinese Academy of Sciences Key Laboratory of On-Orbit Manufacturing and Integration for Space Optics System, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaChinese Academy of Sciences Key Laboratory of On-Orbit Manufacturing and Integration for Space Optics System, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaChinese Academy of Sciences Key Laboratory of On-Orbit Manufacturing and Integration for Space Optics System, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaChinese Academy of Sciences Key Laboratory of On-Orbit Manufacturing and Integration for Space Optics System, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaChinese Academy of Sciences Key Laboratory of On-Orbit Manufacturing and Integration for Space Optics System, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaChinese Academy of Sciences Key Laboratory of On-Orbit Manufacturing and Integration for Space Optics System, Changchun, ChinaTraditional 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.https://www.frontiersin.org/articles/10.3389/fphy.2025.1537756/fullwavefront sensingimage-based wavefront sensingphase retrievalself-supervised learningneural Network |
| spellingShingle | Yuxuan Liu Yuxuan Liu Yuxuan Liu Xiaoquan Bai Xiaoquan Bai Boqian Xu Boqian Xu Chunyue Zhang Chunyue Zhang Yan Gao Yan Gao Shuyan Xu Shuyan Xu Guohao Ju Guohao Ju Training networks without wavefront label for pixel-based wavefront sensing Frontiers in Physics wavefront sensing image-based wavefront sensing phase retrieval self-supervised learning neural Network |
| title | Training networks without wavefront label for pixel-based wavefront sensing |
| title_full | Training networks without wavefront label for pixel-based wavefront sensing |
| title_fullStr | Training networks without wavefront label for pixel-based wavefront sensing |
| title_full_unstemmed | Training networks without wavefront label for pixel-based wavefront sensing |
| title_short | Training networks without wavefront label for pixel-based wavefront sensing |
| title_sort | training networks without wavefront label for pixel based wavefront sensing |
| topic | wavefront sensing image-based wavefront sensing phase retrieval self-supervised learning neural Network |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2025.1537756/full |
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