基于深度学习的计算全息显示进展

Progress of learning-based computer-generated holography

计算全息作为一种三维显示手段,能够基于衍射计算实现对目标光场的精确重建,在元宇宙通讯、AR/VR头戴显示、车载抬头显示等方向均有着重要的应用。如何实现高速且高质量的相位全息图生成是计算全息领域发展的关键问题,也是当前该方向的重要研究课题。近年来,深度学习技术的飞跃式发展为上述问题的解决提供了一条新的技术路径。本文介绍了计算全息技术的基本原理及算法分类,综述了近年来所提出的基于深度学习的计算全息解决方案,比较了各类方案的优势与不足,展望了深度学习技术在计算全息领域的发展与挑战。

As a three-dimensional(3D)display method,computer-generated holography(CGH)can achieve accurate reconstructions of the target light fields based on diffractive optics.It has broad applications in the metaverse,head-mounted display,head-up display,etc.High-speed calculation and high-quality reconstruction of phase-only holograms(POHs)are key issues that should be emphasized in this field.In recent years,the leapfrog development of deep learning has provided a novel path to address this challenge.In this review,the basic principles and classifications of CGH are briefly introduced.Then,the existing CGH methods based on deep learning are summarized.The advantages and disadvantages of various methods are compared.Finally,the possible research directions and challenges of this field are prospected.