Non-convex optimization for inverse problem solving in computer-generated holography

Xiaomeng Sui; Zehao He; Daping Chu; Liangcai Cao

doi:10.1038/s41377-024-01446-w

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Non-convex optimization for inverse problem solving in computer-generated holography

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- Non-convex optimization for inverse problem solving in computer-generated holography
- Non-convex optimization for inverse problem solving in computer-generated holography
- LSA 2024年13卷第8期页码：1464-1486
- Affiliations：
  
  1.Department of Precision Instruments, Tsinghua University, Beijing 100084, China
  2.Department of Engineering, Centre for Photonic Devices and Sensors, University of Cambridge, 9 JJ Thomson Avenue, Cambridge CB3 0FA, UK
  3.Cambridge University Nanjing Centre of Technology and Innovation, 23 Rongyue Road, Jiangbei New Area, Nanjing 210000, China
- Author bio：
  
  Daping Chu (dpc31@cam.ac.uk)
  Liangcai Cao (clc@tsinghua.edu.cn)
- Funds：
- DOI：10.1038/s41377-024-01446-w
  中图分类号：
- 收稿日期：2024-01-04，
  
  修回日期：2024-03-27，
  
  录用日期：2024-04-07，
  
  网络出版日期：2024-07-09，
  
  纸质出版日期：2024-08-31
- Accepted：
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Non-convex optimization for inverse problem solving in computer-generated holography[J]. LSA, 2024,13(8):1464-1486.

Sui, X. M. et al. Non-convex optimization for inverse problem solving in computer-generated holography. Light: Science & Applications, 13, 1464-1486 (2024).
Non-convex optimization for inverse problem solving in computer-generated holography[J]. LSA, 2024,13(8):1464-1486. DOI： 10.1038/s41377-024-01446-w.

Sui, X. M. et al. Non-convex optimization for inverse problem solving in computer-generated holography. Light: Science & Applications, 13, 1464-1486 (2024). DOI： 10.1038/s41377-024-01446-w.

摘要

Abstract

Computer-generated holography is a promising technique that modulates user-defined wavefronts with digital holograms. Computing appropriate holograms with faithful reconstructions is not only a problem closely related to the fundamental basis of holography but also a long-standing challenge for researchers in general fields of optics. Finding the exact solution of a desired hologram to reconstruct an accurate target object constitutes an ill-posed inverse problem. The general practice of single-diffraction computation for synthesizing holograms can only provide an approximate answer

which is subject to limitations in numerical implementation. Various non-convex optimization algorithms are thus designed to seek an optimal solution by introducing different constraints

frameworks

and initializations. Herein

we overview the optimization algorithms applied to computer-generated holography

incorporating principles of hologram synthesis based on alternative projections and gradient descent methods. This is aimed to provide an underlying basis for optimized hologram generation

as well as insights into the cutting-edge developments of this rapidly evolving field for potential applications in virtual reality

augmented reality

head-up display

data encryption

laser fabrication

and metasurface design.

关键词

Keywords

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https://github.com/THUHoloLab/Optimization_algorithms_for_CGH https://github.com/THUHoloLab/Optimization_algorithms_for_CGH .

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