2019年7月
Deep-learning-assisted Hologram Calculation via Low-Sampling Holograms
Proceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019
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- 開始ページ
- 936
- 終了ページ
- 941
- 記述言語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/IIAI-AAI.2019.00188
Digital holograms can be calculated by simulating light wave propagation on a computer. Hologram calculations are used for three-dimensional displays. However, the calculations take a long time, and the data size of the calculated holograms becomes large. This study presents a deep-learning-assisted hologram calculation using low-sampling holograms. We calculate holograms with low-sampling rates, resulting in the acceleration of the hologram calculation and the decrease of the hologram size. However, the low-sampling holograms decrease the quality of the reconstructed images and will occur the aliasing errors when not satisfying the Nyquist rate. The proposed method uses a deep neural network to retrieve the full-sampling holograms from the low-sampling holograms. We show elementary results of the proposed method in numerical simulation.
- リンク情報
- ID情報
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- DOI : 10.1109/IIAI-AAI.2019.00188
- SCOPUS ID : 85080949601