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http://dx.doi.org/10.6109/jkiice.2020.24.12.1588

Generation of optical fringe patterns using deep learning  

Kang, Ji-Won (Department of Electronic Material Engineering, Kwangwoon University)
Kim, Dong-Wook (Department of Electronic Material Engineering, Kwangwoon University)
Seo, Young-Ho (Department of Electronic Material Engineering, Kwangwoon University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.12, 2020 , pp. 1588-1594 More about this Journal
Abstract
In this paper, we discuss a data balancing method for learning a neural network that generates digital holograms using a deep neural network (DNN). Deep neural networks are based on deep learning (DL) technology and use a generative adversarial network (GAN) series. The fringe pattern, which is the basic unit of a hologram to be created through a deep neural network, has very different data types depending on the hologram plane and the position of the object. However, because the criteria for classifying the data are not clear, an imbalance in the training data may occur. The imbalance of learning data acts as a factor of instability in learning. Therefore, it presents a method for classifying and balancing data for which the classification criteria are not clear. And it shows that learning is stabilized through this.
Keywords
GANs; Hologram; Data preprocessing; Imbalance training; Oversampling;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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