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Deep learning wavefront sensing
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AI
DOI：10.1364/OE.27.000240.png)
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摘要
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We present a new class of wavefront sensors by extending their design space based on machine learning. This approach simplifies both the optical hardware and image processing in wavefront sensing. We experimentally demonstrated a variety of image-based wavefront sensing architectures that can directly estimate Zernike coefficients of aberrated wavefronts from a single intensity image by using a convolutional neural network. We also demonstrated that the proposed deep learning wavefront sensor can be trained to estimate wavefront aberrations stimulated by a point source and even extended sources. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Keyword:
PHASE
DIVERSITY
SENSOR
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
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引用论文
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