Zeng Tianjiao, Zhu Yanmin, Lam Edmund Y
Opt Express. 2021 Nov 22;29(24):40572-40593. doi: 10.1364/OE.443367.
Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials in how deep learning can further improve performance and enable new functionalities for DH. Here, we survey recent developments in various DH applications powered by deep learning algorithms. This article starts with a brief introduction to digital holographic imaging, then summarizes the most relevant deep learning techniques for DH, with discussions on their benefits and challenges. We then present case studies covering a wide range of problems and applications in order to highlight research achievements to date. We provide an outlook of several promising directions to widen the use of deep learning in various DH applications.
近年来，深度学习在数字全息术（DH）中的应用取得了前所未有的进展。然而，在深度学习如何进一步提高数字全息术的性能并实现新功能方面，仍有巨大潜力。在此，我们综述了由深度学习算法驱动的各种数字全息术应用的最新进展。本文首先简要介绍数字全息成像，然后总结数字全息术最相关的深度学习技术，并讨论其优点和挑战。接着，我们给出涵盖广泛问题和应用的案例研究，以突出迄今为止的研究成果。我们展望了几个有前景的方向，以扩大深度学习在各种数字全息术应用中的使用。
