방송공학회논문지 (Journal of Broadcast Engineering)

  • 제28권1호
  • /
  • Pages.3-20
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  • 2023
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  • 1226-7953(pISSN)
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  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
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전파 거리에 따른 위상 홀로그램 복원성능 분석 및 BL-ASM 개선 방안 연구

A Study on Reconstruction Performance of Phase-only Holograms with Varying Propagation Distance

  • 차준영 (경희대학교 소프트웨어융합학과) ;
  • 반현민 (경희대학교 컴퓨터공학부) ;
  • 최승미 (경희대학교 컴퓨터공학부) ;
  • 김진웅 (한국전자통신연구원 미디어연구본부) ;
  • 김휘용 (경희대학교 컴퓨터공학부)
  • 투고 : 2022.11.15
  • 심사 : 2023.01.20
  • 발행 : 2023.01.30
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초록

물체의 진폭과 위상 정보가 free space에서 전달되는 과정을 디지털로 계산하여 기록한 것을 컴퓨터 생성 홀로그램(CGH)라고 한다. 이 CGH는 복소 홀로그램의 형태이지만, 이를 Phase-only 공간광 변조기(SLM)를 통해 디스플레이 하기 위해 위상 홀로그램의 형태로변환하게 된다. 본 논문에서는, 물체의 진폭 정보를 위상 정보에 포함시키는 과정에서 DPAC 등 subsampling이 포함된 기법을 사용한다면 위상 홀로그램의 대역폭이 커지며, 그 결과로 복소 홀로그램 복원 시에는 없던 aliasing이 발생할 수 있음을 실험적으로 밝혔다. 또한, 이렇게 aliasing에 의해 복원성능이 저하되는 거리에서도 공간 주파수 범위를 제약하는 방법을 통해 좋은 화질의 위상 홀로그램 생성이 가능함을 보였다.

A computer-generated hologram (CGH) is a digitally calculated and recorded hologram in which the amplitude and phase information of an image is transmitted in free space. The CGH is in the form of a complex hologram, but it is converted into a phase-only hologram to display through a phase-only spatial light modulator (SLM). In this paper, in the process of including the amplitude information of an object in the phase information, when a technique that includes subsampling such as DPAC is used, we showed experimentally that the bandwidth of the phase-only hologram increases, and as a result, aliasing that was not present in the complex hologram can occur. In addition, it was experimentally shown that it is possible to generate a high-quality phase-only hologram by restricting the spatial frequency range even at a distance where the numerical reconstruction performance is degraded by aliasing.

키워드

과제정보

이 논문은 삼성전자미래기술육성센터의 지원을 받아 수행된 연구임(과제번호SRFC-IT2201-03). 이 연구는 2020학년도 경희대학교 연구비 지원에 의한 결과임(KHU-20201116).

참고문헌

  1. GABOR, Dennis. A new microscopic principle. nature, 1948, 161: 777-778. doi: https://doi.org/10.1038/161777a0
  2. ZHANG, Zichen; YOU, Zheng; CHU, Daping. Fundamentals of phase-only liquid crystal on silicon (LCOS) devices. Light: Science & Applications, 2014, 3.10: e213-e213. doi: https://doi.org/10.1038/lsa.2014.94 
  3. OSTEN, Wolfgang, et al. Recent advances in digital holography. Applied optics, 2014, 53.27: G44-G63. doi: https://doi.org/10.1364/photonics.2014.t2c.1
  4. MATSUSHIMA, Kyoji. Formulation of the rotational transformation of wave fields and their application to digital holography. Applied optics, 2008, 47.19: D110-D116. doi: https://doi.org/10.1364/ao.47.00d110
  5. MATSUSHIMA, Kyoji; SHIMOBABA, Tomoyoshi. Band-limited angular spectrum method for numerical simulation of free-space propagation in far and near fields. Optics express, 2009, 17.22: 19662-19673. doi: https://doi.org/10.1364/oe.17.019662
  6. MAIMONE, Andrew; GEORGIOU, Andreas; KOLLIN, Joel S. Holographic near-eye displays for virtual and augmented reality. ACM Transactions on Graphics (Tog), 2017, 36.4: 1-16. doi: https://doi.org/10.1145/3072959.3073624
  7. HSUEH, Chung-Kai; SAWCHUK, Alexander A. Computer-generated double-phase holograms. Applied optics, 1978, 17.24: 3874-3883. doi: https://doi.org/10.1364/ao.17.003874
  8. GERCHBERG, Ralph W. A practical algorithm for the determination of plane from image and diffraction pictures. Optik, 1972, 35.2: 237-246.
  9. HORISAKI, Ryoichi; TAKAGI, Ryosuke; TANIDA, Jun. Deep-learning-generated holography. Applied optics, 2018, 57.14: 3859-3863. doi: https://doi.org/10.1364/ao.57.003859
  10. EYBPOSH, M. Hossein, et al. DeepCGH: 3D computer-generated holography using deep learning. Optics Express, 2020, 28.18: 26636-26650. doi: https://doi.org/10.1364/oe.399624
  11. PENG, Yifan, et al. Neural holography with camera-in-the-loop training. ACM Transactions on Graphics (TOG), 2020, 39.6: 1-14. doi: https://doi.org/10.1145/3414685.3417802
  12. SHI, Liang, et al. Towards real-time photorealistic 3D holography with deep neural networks. Nature, 2021, 591.7849: 234-239. doi: https://doi.org/10.1038/s41586-020-03152-0
  13. AGUSTSSON, Eirikur; TIMOFTE, Radu. Ntire 2017 challenge on single image super-resolution: Dataset and study. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2017. p. 126-135. doi: https://doi.org/10.1109/cvprw.2017.150
  14. SHI, Wenzhe, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 1874-1883. doi: https://doi.org/10.1109/cvpr.2016.207
  15. YU, Ting, et al. Phase dual-resolution networks for a computer-generated hologram. Optics Express, 2022, 30.2: 2378-2389. doi: https://doi.org/10.1364/oe.448996