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231226s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TVCG.2023.3239670
|2 doi
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|a pubmed24n1454.xml
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|a eng
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| 100 |
1 |
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|a Zhong, Chongli
|e verfasserin
|4 aut
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|a Real-Time High-Quality Computer-Generated Hologram Using Complex-Valued Convolutional Neural Network
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 28.06.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Holographic displays are ideal display technologies for virtual and augmented reality because all visual cues are provided. However, real-time high-quality holographic displays are difficult to achieve because the generation of high-quality computer-generated hologram (CGH) is inefficient in existing algorithms. Here, complex-valued convolutional neural network (CCNN) is proposed for phase-only CGH generation. The CCNN-CGH architecture is effective with a simple network structure based on the character design of complex amplitude. A holographic display prototype is set up for optical reconstruction. Experiments verify that state-of-the-art performance is achieved in terms of quality and generation speed in existing end-to-end neural holography methods using the ideal wave propagation model. The generation speed is three times faster than HoloNet and one-sixth faster than Holo-encoder, and the Peak Signal to Noise Ratio (PSNR) is increased by 3 dB and 9 dB, respectively. Real-time high-quality CGHs are generated in 1920 × 1072 and 3840 × 2160 resolutions for dynamic holographic displays
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|a Journal Article
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1 |
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|a Sang, Xinzhu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yan, Binbin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Hui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Duo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Qin, Xiujuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Shuo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ye, Xiaoqian
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 30(2024), 7 vom: 10. Juni, Seite 3709-3718
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnns
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| 773 |
1 |
8 |
|g volume:30
|g year:2024
|g number:7
|g day:10
|g month:06
|g pages:3709-3718
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| 856 |
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|u http://dx.doi.org/10.1109/TVCG.2023.3239670
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