Researching | Learning Model based on Physical Information for Improving Holographic Display with Poor Hardware

Laser & Optoelectronics Progress, Volume. 61, Issue 24, 2409001(2024)

Learning Model based on Physical Information for Improving Holographic Display with Poor Hardware

Yisen Yang^1,2 and Dengfeng Kuang^1,2、*

Author Affiliations

¹Institute of Modern Optics, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China

²Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Tianjin 300350, China

show less

Abstract Get PDF(in Chinese)

Figures&Tables (5)

References (23)

Paper Information

References(23)

[1] Chang C L, Bang K, Wetzstein G et al. Toward the next-generation VR/AR optics: a review of holographic near-eye displays from a human-centric perspective[J]. Optica, 7, 1563-1578(2020).

[2] Kim J, Gopakumar M, Choi S et al. Holographic glasses for virtual reality[C], 33(2022).

[3] Wu J Y, Kim J. Prescription AR: a fully-customized prescription-embedded augmented reality display[J]. Optics Express, 28, 6225-6241(2020).

[4] Yu C, Meng X Y, Jiang L et al. Holographic near-eye display system with expanded eyebox based on waveguide[J]. Acta Optica Sinica, 43, 0909001(2023).

[5] Wang X S, Gui J B, Li J C et al. Comparative study of phase-only hologram generation algorithms based on iteration[J]. Laser & Optoelectronics Progress, 60, 0609001(2023).

[6] Pi D P, Liu J, Wang Y T. Review of computer-generated hologram algorithms for color dynamic holographic three-dimensional display[J]. Light: Science & Applications, 11, 231(2022).

[7] Gerchberg R. A practical algorithm for the determination of phase from image and diffraction plane pictures[J]. Optik, 35, 237-246(1972).

[8] Hu C, Sun G B, Jiang S L et al. Improved weighted iterative multi-plane holographic display method[J]. Acta Optica Sinica, 43, 2309001(2023).

[9] Maimone A, Georgiou A, Kollin J S. Holographic near-eye displays for virtual and augmented reality[J]. ACM Transactions on Graphics, 36, 85(2017).

[10] Chakravarthula P, Tseng E, Srivastava T et al. Learned hardware-in-the-loop phase retrieval for holographic near-eye displays[J]. ACM Transactions on Graphics, 39, 186(2020).

[11] Peng Y F, Choi S, Padmanaban N et al. Neural holography with camera-in-the-loop training[J]. ACM Transactions on Graphics, 39, 185(2020).

[12] Chen C, Lee B, Li N N et al. Multi-depth hologram generation using stochastic gradient descent algorithm with complex loss function[J]. Optics Express, 29, 15089-15103(2021).

[13] Xia X X, Yang F R, Wang W S et al. Investigating learning-empowered hologram generation for holographic displays with ill-tuned hardware[J]. Optics Letters, 48, 1478-1481(2023).

[14] Kavaklı K, Urey H, Akşit K. Learned holographic light transport: invited[J]. Applied Optics, 61, B50-B55(2022).

[15] Choi S, Gopakumar M, Peng Y F et al. Neural 3D holography: learning accurate wave propagation models for 3D holographic virtual and augmented reality displays[J]. ACM Transactions on Graphics, 40, 240(2021).

[16] Matsushima K, Shimobaba T. Band-limited angular spectrum method for numerical simulation of free-space propagation in far and near fields[J]. Optics Express, 17, 19662-19673(2009).

[17] Goodman J W, Cox M E. Introduction to Fourier optics[J]. Physics Today, 22, 97-101(1969).

[18] Tseng E, Yu F, Yang Y T et al. Hyperparameter optimization in black-box image processing using differentiable proxies[J]. ACM Transactions on Graphics, 38, 27(2019).

[19] Palima D, Daria V R. Effect of spurious diffraction orders in arbitrary multifoci patterns produced via phase-only holograms[J]. Applied Optics, 45, 6689-6693(2006).

[20] Arrizón V, Carreón E, Testorf M. Implementation of Fourier array illuminators using pixelated SLM: efficiency limitations[J]. Optics Communications, 160, 207-213(1999).

[21] Wang F W, Lazarov B S, Sigmund O. On projection methods, convergence and robust formulations in topology optimization[J]. Structural and Multidisciplinary Optimization, 43, 767-784(2011).

[22] Agustsson E, Timofte R. NTIRE 2017 challenge on single image super-resolution: dataset and study[C], 1122-1131(2017).

[23] Kingma D P, Ba J. Adam: a method for stochastic optimization[EB/OL]. https://arxiv.org/abs/1412.6980

Tools

Get Citation

Copy Citation Text

Yisen Yang, Dengfeng Kuang. Learning Model based on Physical Information for Improving Holographic Display with Poor Hardware[J]. Laser & Optoelectronics Progress, 2024, 61(24): 2409001

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Holography

Received: Mar. 18, 2024

Accepted: May. 20, 2024

Published Online: Nov. 18, 2024

The Author Email: Dengfeng Kuang (dfkuang@nankai.edu.cn)

DOI:10.3788/LOP240916

CSTR:32186.14.LOP240916

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology