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Related Experiment Video

Updated: Sep 10, 2025

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

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

Published on: September 16, 2022

Propagation-adaptive 4K computer-generated holography using physics-constrained spatial and Fourier neural operator.

Ninghe Liu¹, Kexuan Liu², Yixin Yang²

¹Weiyang College, Tsinghua University, Beijing, 100084, China.

Nature Communications

|August 20, 2025

Summary

This study introduces a novel deep learning solver for computer-generated holography (CGH) that adapts to different propagation distances. This breakthrough enables faster, more flexible holographic displays without hardware changes.

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Last Updated: Sep 10, 2025

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Area of Science:

Optics and Photonics
Computer Science
Artificial Intelligence

Background:

Computer-generated holography (CGH) aims for realistic object reconstructions.
Current deep learning CGH models often lack adaptability due to fixed propagation distances.

Purpose of the Study:

To develop a deep learning-based CGH solver adaptable to varying propagation distances.
To enhance the flexibility and practical applicability of algorithmic CGH synthesis.

Main Methods:

Introduced a spatial and Fourier neural operator (SFO-solver) for CGH synthesis.
Encoded target intensity and propagation distance as network inputs, leveraging physical insights of optical diffraction.
Achieved high-speed 4K CGH synthesis.

Main Results:

The SFO-solver demonstrated high-speed 4K CGH synthesis at 0.16 seconds per frame.
Achieved an average PSNR of 39.25 dB across a 30 mm depth range.
Experimentally validated various-depth 2D holographic projection and adjustable multi-plane 3D display.

Conclusions:

The SFO-solver significantly improves the flexibility of deep learning-based CGH.
Provides a scalable foundation for dynamic refocusing and interactive holographic displays.
Demonstrates practical, hardware-independent holographic projection capabilities.