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Wavefront sensing with optical differentiation powered by deep learning
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DOI：10.1364/OL.530559.png)
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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.
Keyword:
PHASE OBJECTS
VISUALIZATION
SENSOR
期刊
IF:
3.3
论文数:
4.0W
被引数:
7.6W
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引用论文
Analysis of the signal-to-noise ratio in the optical differentiation wavefront sensor.png)
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