Efficient Computer-Generated Holography Based on Mixed Linear Convolutional Neural Networks

被引:2
|
作者
Xu, Xianfeng [1 ]
Wang, Xinwei [1 ]
Luo, Weilong [1 ]
Wang, Hao [1 ]
Sun, Yuting [1 ]
机构
[1] China Univ Petr East China, Coll Sci, Qingdao 266580, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 09期
关键词
digital holography; computer-generated holography (CGH); deep learning; image reconstruction; neural network; SEGMENTATION;
D O I
10.3390/app12094177
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Imaging based on computer-generated holography using traditional methods has the problems of poor quality and long calculation cycles. However, recently, the development of deep learning has provided new ideas for this problem. Here, an efficient computer-generated holography (ECGH) method is proposed for computational holographic imaging. This method can be used for computational holographic imaging based on mixed linear convolutional neural networks (MLCNN). By introducing fully connected layers in the network, the suggested design is more powerful and efficient at information mining and information exchange. Using the ECGH, the pure phase image required can be obtained after calculating the custom light field. Compared with traditional computed holography based on deep learning, the method used here can reduce the number of network parameters needed for network training by about two-thirds while obtaining a high-quality image in the reconstruction, and the network structure has the potential to solve various image-reconstruction problems.
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页数:10
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