Abstract

Recent studies have shown convolutional neural networks (CNNs) can be trained to perform modal decomposition using intensity images of optical fields. A fundamental limitation of these techniques is that the modal phases cannot be uniquely calculated using a single intensity image. The knowledge of modal phases is crucial for wavefront sensing, alignment, and mode matching applications. Heterodyne imaging techniques can provide images of the transverse complex amplitude and phase profiles of laser beams at high resolutions and frame rates. In this work, we train a CNN to perform modal decomposition using simulated heterodyne images, allowing the complete modal phases to be predicted. This is, to our knowledge, the first machine learning decomposition scheme to utilize complex phase information to perform modal decomposition. We compare our network with a traditional overlap integral and center-of-mass centering algorithm and show that it is both less sensitive to beam centering and on average more accurate in our simulated images.

© 2021 Optical Society of America

Full Article  |  PDF Article
More Like This
Pattern recognition in distributed fiber-optic acoustic sensor using an intensity and phase stacked convolutional neural network with data augmentation

Huan Wu, Bin Zhou, Kun Zhu, Chao Shang, Hwa-Yaw Tam, and Chao Lu
Opt. Express 29(3) 3269-3283 (2021)

Hermite–Gaussian mode detection via convolution neural networks

L. R. Hofer, L. W. Jones, J. L. Goedert, and R. V. Dragone
J. Opt. Soc. Am. A 36(6) 936-943 (2019)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (7)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (1)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (13)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription