全息术
网(多面体)
计算机科学
光学
过程(计算)
深度学习
人工智能
数字全息术
全息显示器
相(物质)
迭代重建
数字全息显微术
物理
数学
量子力学
操作系统
几何学
作者
Kaiqiang Wang,Jiazhen Dou,Qian Kemao,Jianglei Di,Jianlin Zhao
出处
期刊:Optics Letters
[The Optical Society]
日期:2019-09-19
卷期号:44 (19): 4765-4765
被引量:158
摘要
In this Letter, for the first time, to the best of our knowledge, we propose a digital holographic reconstruction method with a one-to-two deep learning framework (Y-Net). Perfectly fitting the holographic reconstruction process, the Y-Net can simultaneously reconstruct intensity and phase information from a single digital hologram. As a result, this compact network with reduced parameters brings higher performance than typical network variants. The experimental results of the mouse phagocytes demonstrate the advantages of the proposed Y-Net.
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