深度学习生成的全息术。
Deep-learning-generated holography.
作者信息
Horisaki Ryoichi, Takagi Ryosuke, Tanida Jun
出版信息
Appl Opt. 2018 May 10;57(14):3859-3863. doi: 10.1364/AO.57.003859.
PMID:29791353
Abstract
We present a method for computer-generated holography based on deep learning. The inverse process of light propagation is regressed with a number of computationally generated speckle data sets. This method enables noniterative calculation of computer-generated holograms (CGHs). The proposed method was experimentally verified with a phase-only CGH.
摘要
我们提出了一种基于深度学习的计算机生成全息术方法。利用多个通过计算生成的散斑数据集对光传播的逆过程进行回归。该方法能够对计算机生成全息图（CGH）进行非迭代计算。所提出的方法通过仅相位CGH进行了实验验证。
Zotero 插件