Wavefront sensing with optical differentiation powered by deep learning
We report the experimental demonstration of an optical differentiation wavefront sensor (ODWS) based on binary pixelated linear and nonlinear amplitude filtering in the far-field. We trained and tested a convolutional neural network that reconstructs the spatial phase map from nonlinear-filter-based...
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| Veröffentlicht in: | Optics letters 2024-09, Vol.49 (18), p.5216 |
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| container_title | Optics letters |
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| creator | Swain, Biswa R Akif Qadeer, M Dorrer, Christophe Narayanan, Renuka Manjula Rolland, Jannick P Qiao, Jie |
| description | We report the experimental demonstration of an optical differentiation wavefront sensor (ODWS) based on binary pixelated linear and nonlinear amplitude filtering in the far-field. We trained and tested a convolutional neural network that reconstructs the spatial phase map from nonlinear-filter-based ODWS data for which an analytic reconstruction algorithm is not available. It shows accurate zonal retrieval over different magnitudes of wavefronts and on randomly shaped wavefronts. This work paves the way for the implementation of simultaneously sensitive, high dynamic range, and high-resolution wavefront sensing. |
| doi_str_mv | 10.1364/OL.530559 |
| format | Article |
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| ispartof | Optics letters, 2024-09, Vol.49 (18), p.5216 |
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| language | eng |
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| source | Optica Publishing Group Journals |
| subjects | Algorithms Artificial neural networks Differentiation Machine learning Wave front sensors Wave fronts |
| title | Wavefront sensing with optical differentiation powered by deep learning |
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