Deep-Learning Computational Holography: A Review (Invited)

T. Shimobaba, David Blinder, Tobias Birnbaum, I. Hoshi, Harutaka Shiomi, P. Schelkens, T. Ito
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引用次数: 22

Abstract

Deep learning has been developing rapidly, and many holographic applications have been investigated using deep learning. They have shown that deep learning can outperform previous physically-based calculations using lightwave simulation and signal processing. This review focuses on computational holography, including computer-generated holograms, holographic displays, and digital holography, using deep learning. We also discuss our personal views on the promise, limitations and future potential of deep learning in computational holography.
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深度学习计算全息:综述(特邀)
深度学习发展迅速,利用深度学习研究了许多全息应用。他们已经表明,使用光波模拟和信号处理,深度学习可以胜过以前基于物理的计算。本文综述了使用深度学习的计算全息术,包括计算机生成的全息图、全息显示器和数字全息术。我们还讨论了我们对计算全息中深度学习的前景、局限性和未来潜力的个人看法。
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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