Real-Time High-Quality Computer-Generated Hologram Using Complex-Valued Convolutional Neural Network
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 existin...
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 7 vom: 10. Juni, Seite 3709-3718 |
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| Weitere Verfasser: | , , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics |
| Schlagworte: | Journal Article |
| Zusammenfassung: | 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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| Beschreibung: | Date Revised 28.06.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0506 |
| DOI: | 10.1109/TVCG.2023.3239670 |