串扰
随机梯度下降算法
全息术
计算机科学
多路复用
全息显示器
光学
基点
最速下降法
梯度下降
算法
人工智能
物理
数学优化
人工神经网络
电信
数学
作者
Zi Wang,Tao Chen,Qiyang Chen,Kefeng Tu,Qibin Feng,Guo‐Jiao Lv,Anting Wang,Hai Ming
出处
期刊:Optics Express
[The Optical Society]
日期:2023-02-14
卷期号:31 (5): 7413-7413
被引量:10
摘要
Multi-plane reconstruction is essential for realizing a holographic three-dimensional (3D) display. One fundamental issue in conventional multi-plane Gerchberg-Saxton (GS) algorithm is the inter-plane crosstalk, mainly caused by the neglect of other planes’ interference in the process of amplitude replacement at each object plane. In this paper, we proposed the time-multiplexing stochastic gradient descent (TM-SGD) optimization algorithm to reduce the multi-plane reconstruction crosstalk. First, the global optimization feature of stochastic gradient descent (SGD) was utilized to reduce the inter-plane crosstalk. However, the crosstalk optimization effect would degrade as the number of object planes increases, due to the imbalance between input and output information. Thus, we further introduced the time-multiplexing strategy into both the iteration and reconstruction process of multi-plane SGD to increase input information. In TM-SGD, multiple sub-holograms are obtained through multi-loop iteration and then sequentially refreshed on spatial light modulator (SLM). The optimization condition between the holograms and the object planes converts from one-to-many to many-to-many, improving the optimization of inter-plane crosstalk. During the persistence of vision, multiple sub-hologram jointly reconstruct the crosstalk-free multi-plane images. Through simulation and experiment, we confirmed that TM-SGD could effectively reduce the inter-plane crosstalk and improve image quality.The proposed TM-SGD-based holographic display has wide applications in tomographic 3D visualization for biology, medical science, and engineering design, which need to reconstruct multiple independent tomographic images without inter-plane crosstalk.
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