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http://dx.doi.org/10.5909/JBE.2020.25.6.935

Deep Learning-based Super Resolution for Phase-only Holograms  

Kim, Woosuk (Kwangwoon university Electronic Materials Engineering)
Park, Byung-Seo (Kwangwoon university Electronic Materials Engineering)
Kim, Jin-Kyum (Kwangwoon university Electronic Materials Engineering)
Oh, Kwan-Jung (ETRI)
Kim, Jin-Woong (ETRI)
Kim, Dong-Wook (Kwangwoon university Electronic Materials Engineering)
Seo, Young-Ho (Kwangwoon university Electronic Materials Engineering)
Publication Information
Journal of Broadcast Engineering / v.25, no.6, 2020 , pp. 935-943 More about this Journal
Abstract
In this paper, we propose a method using deep learning for high-resolution display of phase holograms. If a general interpolation method is used, the brightness of the reconstruction result is lowered, and noise and afterimages occur. To solve this problem, a hologram was trained with a neural network structure that showed good performance in the single-image super resolution (SISR). As a result, it was possible to improve the problem that occurred in the reconstruction result and increase the resolution. In addition, by adjusting the number of channels to increase performance, the result increased by more than 0.3dB in same training.
Keywords
hologram; deep learning; super resolution; convolutional neural network; image processing;
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