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Cited by 253

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Deep learning wavefront sensing

Open access
Nishizaki, Yohei, Valdivia, Matias, Horisaki, Ryoichi, Kitaguchi, Katsuhisa, Saito, Mamoru, Tanida, Jun, Vera, Esteban·2019·Optics Express
DOI 10.1364/oe.27.000240
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Abstract

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.

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Crossref · 10.1364/oe.27.000240

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Metadata

Authors
Nishizaki, Y., Valdivia, M., Horisaki, R., Kitaguchi, K., Saito, M., Tanida, J., & Vera, E.
Venue
Optics Express
Publisher
Optica Publishing Group
Published
2019-01-04
Volume
27
Issue
1
Pages
240
ISSN
1094-4087 (ISSN-L)
DOI
10.1364/oe.27.000240
OpenAlex
W2907716704
Cited by
253
References
13
FWCI
13.56
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Deep learning wavefront sensing. https://app.cochat.ai/papers/10.1364/oe.27.000240
The paper
Nishizaki, Y., Valdivia, M., Horisaki, R., Kitaguchi, K., Saito, M., Tanida, J., & Vera, E. (2019). Deep learning wavefront sensing. Optics Express. https://doi.org/10.1364/oe.27.000240

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.