期刊
OPTICS EXPRESS
卷 27, 期 1, 页码 240-251出版社
Optica Publishing Group
DOI: 10.1364/OE.27.000240
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类别
资金
- Japan Society for the Promotion of Science [JP17H02799, JP17K00233]
- Precursory Research for Embryonic Science and Technology [JPMJPR17PB]
- Fondo Nacional de Ciencia y Tecnologia [1181943]
- Redes Etapa Inicial [REDI170539]
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
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