Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Light Acquisition02:16

Light Acquisition

8.0K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Spatial incoherence-driven optical reconstruction of holograms with observer shift-invariance.

Light, science & applications·2025
Same author

Direct amplitude-only hologram realized by broken symmetry.

Science advances·2024
Same author

Enhancing efficiency of complex field encoding for amplitude-only spatial light modulator based on a neural network.

Optics express·2023
Same author

Deep learning-based incoherent holographic camera enabling acquisition of real-world holograms for holographic streaming system.

Nature communications·2023
Same author

Diffraction-engineered holography: Beyond the depth representation limit of holographic displays.

Nature communications·2022
Same author

Observation of scalable sub-Poissonian-field lasing in a microlaser.

Scientific reports·2019

Related Experiment Video

Updated: May 6, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.7K

Enhancing light efficiency in phase-only holograms via neural network.

Balakiruthika Periyasamy1, Heeseong Hwang1, Daeho Yang2

  • 1Department of Physics, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Korea.

Scientific Reports
|October 14, 2025
PubMed
Summary

ShuffleResnet, a novel neural network, enhances hologram reconstruction efficiency by 59% and improves image quality. This artificial neural network approach offers faster processing for real-time holographic applications.

More Related Videos

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
08:39

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Published on: January 28, 2019

10.3K
Evaluation and Manipulation of Neural Activity Using Two-Photon Holographic Microscopy
10:09

Evaluation and Manipulation of Neural Activity Using Two-Photon Holographic Microscopy

Published on: September 16, 2022

3.2K

Related Experiment Videos

Last Updated: May 6, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.7K
Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
08:39

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Published on: January 28, 2019

10.3K
Evaluation and Manipulation of Neural Activity Using Two-Photon Holographic Microscopy
10:09

Evaluation and Manipulation of Neural Activity Using Two-Photon Holographic Microscopy

Published on: September 16, 2022

3.2K

Area of Science:

  • Optics and Photonics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Conventional double phase encoding methods (DPM) for phase-only spatial light modulators (SLMs) face limitations in light efficiency and reconstruction quality.
  • Artificial neural networks (ANNs) show promise in advancing hologram synthesis and reconstruction.

Purpose of the Study:

  • To introduce ShuffleResnet, a neural phase encoding approach to overcome DPM limitations.
  • To enhance light efficiency and reconstruction fidelity in holographic applications.

Main Methods:

  • Developed ShuffleResnet, a neural phase encoding model.
  • Utilized numerical simulations to evaluate performance against conventional DPM.
  • Tested hologram encoding at 1920x1080 resolution.

Main Results:

  • Achieved a 59% increase in light efficiency compared to DPM.
  • Demonstrated improved reconstruction quality and artifact suppression.
  • Attained an average inference speed of 4.74 milliseconds per hologram.

Conclusions:

  • ShuffleResnet significantly enhances light efficiency and reconstruction fidelity.
  • The model's fast inference speed indicates strong potential for real-time holographic applications.
  • This neural network approach offers a superior alternative for phase-only SLM-based holography.