Deep holography

G. Situ
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引用次数: 17

Abstract

With the explosive growth of mathematical optimization and computing hardware, deep neural networks (DNN) have become tremendously powerful tools to solve many challenging problems in various fields, ranging from decision making to computational imaging and holography. In this manuscript, I focus on the prosperous interactions between DNN and holography. On the one hand, DNN has been demonstrated to be in particular proficient for holographic reconstruction and computer-generated holography almost in every aspect. On the other hand, holography is an enabling tool for the optical implementation of DNN the other way around owing to the capability of interconnection and light speed processing in parallel. The purpose of this article is to give a comprehensive literature review on the recent progress of deep holography , an emerging interdisciplinary research field that is mutually inspired by holography and DNN. I first give a brief overview of the basic theory and architectures of DNN, and then discuss some of the most important progresses of deep holography. I hope that the present unified exposition will stimulate further development in this promising and exciting field of research.
深全息术
随着数学优化和计算硬件的爆炸式增长,深度神经网络(DNN)已经成为解决从决策到计算成像和全息等各个领域许多具有挑战性问题的强大工具。在这篇手稿中,我着重于深度神经网络和全息术之间的繁荣相互作用。一方面,DNN已被证明在几乎所有方面都特别精通全息重建和计算机生成的全息。另一方面,由于全息术的互连能力和并行的光速处理能力,它是DNN光学实现的一种使能工具。深度全息术是受全息术和深度神经网络相互启发的新兴跨学科研究领域,本文对其最新进展进行了全面的文献综述。本文首先简要介绍深度神经网络的基本理论和结构,然后讨论深度全息的一些最重要的进展。我希望目前的统一阐述将促进这一有前途和令人兴奋的研究领域的进一步发展。
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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CiteScore
10.90
自引率
0.00%
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