Abstract

This paper proposes a particle volume reconstruction directly from an in-line hologram using a deep neural network (DNN). Digital holographic volume reconstruction conventionally uses multiple diffraction calculations to obtain sectional reconstructed images from an in-line hologram, followed by detection of the lateral and axial positions, and the sizes of particles by using focus metrics. However, the axial resolution is limited by the numerical aperture of the optical system, and the processes are time consuming. The method proposed here can simultaneously detect the lateral and axial positions, and the particle sizes via a DNN. We numerically investigated the performance of the DNN in terms of the errors in the detected positions and sizes. The calculation time is faster than conventional diffracted-based approaches.

© 2019 Optical Society of America

Full Article  |  PDF Article
More Like This
Focus prediction in digital holographic microscopy using deep convolutional neural networks

Tomi Pitkäaho, Aki Manninen, and Thomas J. Naughton
Appl. Opt. 58(5) A202-A208 (2019)

Fast particle characterization using digital holography and neural networks

B. Schneider, J. Dambre, and P. Bienstman
Appl. Opt. 55(1) 133-139 (2016)

Holographic 3D particle reconstruction using a one-stage network

Yunping Zhang, Yanmin Zhu, and Edmund Y. Lam
Appl. Opt. 61(5) B111-B120 (2022)

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

Supplementary Material (4)

NameDescription
Visualization 1       Visualization 1 is the movie for the case of 40 particles.
Visualization 2       Visualization 2 is the movie for the case of 73 particles.
Visualization 3       Visualization 3 is the movie for the case of 116 particles.
Visualization 4       Visualization 4 is the movie for the case of 163 particles.

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 (9)

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 (2)

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 (3)

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

Metrics