Continuous Depth Control of Phase-Only Hologram With Depth Embedding Block
Digital holography is a promising candidate for advanced display, although several obstacles remain, such as the problem of heavy time consumption in the generation of phase-only holograms. Recently, deep-learning-based methods have achieved the real-time generation of holograms while maintaining hi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9739941/ |
| _version_ | 1811304080806510592 |
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| author | Won Jong Ryu Jin Su Lee Yong Hyub Won |
| author_facet | Won Jong Ryu Jin Su Lee Yong Hyub Won |
| author_sort | Won Jong Ryu |
| collection | DOAJ |
| description | Digital holography is a promising candidate for advanced display, although several obstacles remain, such as the problem of heavy time consumption in the generation of phase-only holograms. Recently, deep-learning-based methods have achieved the real-time generation of holograms while maintaining high image quality. However, the holograms created with deep neural networks can reproduce images only at a specific distance because their target depth is fixed in the training process. This paper suggested and demonstrated a deep neural network that can continuously control the depth of the phase-only hologram. The network takes a target depth and an input image and generates a phase-only hologram. We added a depth embedding block that moves the hologram latent vector depending on the target depth. Thus, we can change the location of the image plane without retraining. The numerical and optical experiments show that the network understands the relationship between the depth and the appearance of the phase-only hologram. As a result, phase-only holograms generated with the proposed network can reconstruct images with around 25-dB PSNR. |
| first_indexed | 2024-04-13T08:00:35Z |
| format | Article |
| id | doaj.art-d38bb83792b7473d8962737d35bf4cd4 |
| institution | Directory Open Access Journal |
| issn | 1943-0655 |
| language | English |
| last_indexed | 2024-04-13T08:00:35Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj.art-d38bb83792b7473d8962737d35bf4cd42022-12-22T02:55:18ZengIEEEIEEE Photonics Journal1943-06552022-01-011421710.1109/JPHOT.2022.31612259739941Continuous Depth Control of Phase-Only Hologram With Depth Embedding BlockWon Jong Ryu0https://orcid.org/0000-0003-2942-271XJin Su Lee1https://orcid.org/0000-0002-4335-9166Yong Hyub Won2https://orcid.org/0000-0001-8900-8698School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of KoreaSpatial Optical Information Research Center, Korea Photonics Technology Institute, Gwangju, Republic of KoreaSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of KoreaDigital holography is a promising candidate for advanced display, although several obstacles remain, such as the problem of heavy time consumption in the generation of phase-only holograms. Recently, deep-learning-based methods have achieved the real-time generation of holograms while maintaining high image quality. However, the holograms created with deep neural networks can reproduce images only at a specific distance because their target depth is fixed in the training process. This paper suggested and demonstrated a deep neural network that can continuously control the depth of the phase-only hologram. The network takes a target depth and an input image and generates a phase-only hologram. We added a depth embedding block that moves the hologram latent vector depending on the target depth. Thus, we can change the location of the image plane without retraining. The numerical and optical experiments show that the network understands the relationship between the depth and the appearance of the phase-only hologram. As a result, phase-only holograms generated with the proposed network can reconstruct images with around 25-dB PSNR.https://ieeexplore.ieee.org/document/9739941/Holographyphase-only hologramdeep neural networkdepth embedding blockhologram latent space |
| spellingShingle | Won Jong Ryu Jin Su Lee Yong Hyub Won Continuous Depth Control of Phase-Only Hologram With Depth Embedding Block IEEE Photonics Journal Holography phase-only hologram deep neural network depth embedding block hologram latent space |
| title | Continuous Depth Control of Phase-Only Hologram With Depth Embedding Block |
| title_full | Continuous Depth Control of Phase-Only Hologram With Depth Embedding Block |
| title_fullStr | Continuous Depth Control of Phase-Only Hologram With Depth Embedding Block |
| title_full_unstemmed | Continuous Depth Control of Phase-Only Hologram With Depth Embedding Block |
| title_short | Continuous Depth Control of Phase-Only Hologram With Depth Embedding Block |
| title_sort | continuous depth control of phase only hologram with depth embedding block |
| topic | Holography phase-only hologram deep neural network depth embedding block hologram latent space |
| url | https://ieeexplore.ieee.org/document/9739941/ |
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