Related Experiment Video
Updated: May 12, 2026
10:16
Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
Published on: February 8, 2014
12.2K
On the use of deep learning for computer-generated holography.
Xuan Yu1, Haomiao Zhang2,3, Zhe Zhao1
1Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
Iscience
|June 10, 2025
Summary
Deep learning is revolutionizing computer-generated holography (CGH), enabling high-quality, real-time holographic displays. This review explores deep-learning-based CGH (DLCGH) models and future prospects.
Area of Science:
- Optics and Photonics
- Artificial Intelligence
- Computer Science
Background:
- Computer-generated holography (CGH) and machine learning have independently advanced significantly.
- Recent breakthroughs in deep learning have accelerated progress in CGH, particularly for display applications.
Purpose of the Study:
- To review the integration of deep learning into CGH.
- To examine the evolution and current state of deep-learning-based CGH (DLCGH).
- To explore emerging research frontiers in DLCGH.
Main Methods:
- Introduction to fundamental concepts of CGH and deep learning.
- Analysis of the development trajectory of DLCGH.
- Exploration of various DLCGH model categories: data-driven, physics-driven, and jointly optimized.
Main Results:
- Deep learning has demonstrated remarkable success in enhancing CGH.
- Significant strides have been made towards achieving high-quality and real-time holographic displays.
- DLCGH encompasses diverse modeling approaches, each with unique advantages.
Conclusions:
- The synergy between deep learning and CGH offers substantial potential for advanced holographic technologies.
- Future research should focus on overcoming current challenges and exploring new opportunities in DLCGH.
- DLCGH is poised to be a key area for future innovation in holographic displays.
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