Abstract

Holography is perhaps the most promising technology to achieve wide field of view compact eye-glasses style near-eye displays. However, the digital hologram computation algorithms are still not perfect and resort to heuristic encoding or iterative methods relying on varying relaxations. In this paper, we deviate from such heuristic solutions to holographic phase retrieval but instead rely on formal optimization that is enabled by complex Wirtinger gradients. We pose the entire hologram computation forward model as a differentiable forward model and formulate a quadratic loss function that is solved via firstorder optimization methods. Using this framework, we achieve holographic reconstructions with an order of magnitude improved image quality, both in simulation and on an experimental prototype.

© 2020 The Author(s)

PDF Article  |   Presentation Video
More Like This
High-Speed Computer-Generated Holography Using Convolutional Neural Networks

M. Hossein Eybposh, Nicholas W. Caira, Praneeth Chakravarthula, Mathew Atisa, and Nicolas C. Pégard
BTu2C.2 Optics and the Brain (BRAIN) 2020

Enhanced Two-photon Absorption with Deep Learning-based Computer Generated Holography

M. Hossein Eybposh, Nicholas W. Caira, Matthew Atisa, Praneeth Chakravarthula, and Nicolas C. Pégard
FTu2B.2 Frontiers in Optics (FiO) 2020

Computer Generated Holography with Depth-based View Synthesis

Fachada Sarah and Lafruit Gauthier
HF1D.7 Digital Holography and Three-Dimensional Imaging (DH) 2020

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 OSA member, or as an authorized user of your institution.

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

Presentation Video

Presentation video access is available to:

  1. OSA Publishing subscribers
  2. Technical meeting attendees
  3. OSA members who wish to use one of their free downloads. Please download the article first. After downloading, please refresh this page.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription or free downloads


More Like This
High-Speed Computer-Generated Holography Using Convolutional Neural Networks

M. Hossein Eybposh, Nicholas W. Caira, Praneeth Chakravarthula, Mathew Atisa, and Nicolas C. Pégard
BTu2C.2 Optics and the Brain (BRAIN) 2020

Enhanced Two-photon Absorption with Deep Learning-based Computer Generated Holography

M. Hossein Eybposh, Nicholas W. Caira, Matthew Atisa, Praneeth Chakravarthula, and Nicolas C. Pégard
FTu2B.2 Frontiers in Optics (FiO) 2020

Computer Generated Holography with Depth-based View Synthesis

Fachada Sarah and Lafruit Gauthier
HF1D.7 Digital Holography and Three-Dimensional Imaging (DH) 2020