基于深度学习的多尺度数字全息图重建

Multiscale Digital Hologram Reconstruction Based on Deep Learning

针对单个深度学习模型不能对多尺度的数字全息图进行波前重建的问题,提出一种基于U-Net模型的改进网络结构,模拟数字全息成像过程,生成不同尺度的全息图像作为数据集。将不同尺度的数字全息图分别用于训练网络的不同部分,得到一个可以对三种不同尺度的数字全息图波前信息进行重建的深度学习模型。实验结果表明,所提网络结构可对不同尺度的数字全息图进行重建,获得准确的数字全息图波前信息。研究内容为使用单个深度学习模型处理多种尺度的数字全息图这类问题提供了一种解决思路。

To address the problem of a single deep-learning model being unable to reconstruct the wavefront of digital holograms with multiple scales,an improved network structure based on the U-Net model is proposed to simulate the digital holographic imaging process and generate holographic images with different scales as data sets.Digital holograms with different scales are used in different parts of the training network,and a depth learning model is obtained,which can reconstruct the wavefront information of digital holograms with three different scales.The experimental results show that the proposed network structure can reconstruct digital holograms with various scales and obtain accurate wavefront information of digital holograms.The research content solves the problem of using a single deep-learning model to deal with digital holograms with varying scales.