On the use of deep learning for computer-generated holography

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2025-04-23 DOI:10.1016/j.isci.2025.112507

Xuan Yu , Haomiao Zhang , Zhe Zhao , Xuhao Fan , Shaodong Hu , Zongjing Li , Wenbin Chen , Daqian Li , Shaoxi Shi , Wei Xiong , Hui Gao

引用次数: 0

Abstract

The research disciplines of computer-generated holography (CGH) and machine learning have evolved in parallel for decades and experienced booming growth due to breakthroughs in mathematical optimization and computing hardware. Over the past few years, deep learning has been applied to CGH and achieved remarkable success, accustoming a great step toward high-quality and real-time holographic display. This review introduces the fundamental concepts of CGH and deep learning, examines the development of deep-learning–based computer-generated holography (DLCGH), and explores cutting-edge research frontiers including data-driven models, physics-driven models, and jointly optimized models. Finally, we summarize with an outlook on the challenges and prospects of DLCGH.

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关于在计算机生成全息术中使用深度学习

计算机生成全息（CGH）和机器学习的研究学科已经并行发展了几十年，并且由于数学优化和计算硬件的突破而经历了蓬勃发展。近年来，深度学习在全息显示领域的应用取得了显著的成功，向高质量、实时的全息显示迈出了一大步。本文介绍了CGH和深度学习的基本概念，综述了基于深度学习的计算机生成全息（dlgh）的研究进展，并探讨了数据驱动模型、物理驱动模型和联合优化模型等前沿研究领域。最后，总结并展望了DLCGH面临的挑战和发展前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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