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Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction

In this paper, we propose a method to generate multi-depth phase-only holograms using stochastic gradient descent (SGD) algorithm with weighted complex loss function and masked multi-layer diffraction. The 3D scene can be represented by a combination of layers in different depths. In the wave propag...

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Autores principales: Quan, Jiale, Yan, Binbin, Sang, Xinzhu, Zhong, Chongli, Li, Hui, Qin, Xiujuan, Xiao, Rui, Sun, Zhi, Dong, Yu, Zhang, Huming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056174/
https://www.ncbi.nlm.nih.gov/pubmed/36985013
http://dx.doi.org/10.3390/mi14030605
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author Quan, Jiale
Yan, Binbin
Sang, Xinzhu
Zhong, Chongli
Li, Hui
Qin, Xiujuan
Xiao, Rui
Sun, Zhi
Dong, Yu
Zhang, Huming
author_facet Quan, Jiale
Yan, Binbin
Sang, Xinzhu
Zhong, Chongli
Li, Hui
Qin, Xiujuan
Xiao, Rui
Sun, Zhi
Dong, Yu
Zhang, Huming
author_sort Quan, Jiale
collection PubMed
description In this paper, we propose a method to generate multi-depth phase-only holograms using stochastic gradient descent (SGD) algorithm with weighted complex loss function and masked multi-layer diffraction. The 3D scene can be represented by a combination of layers in different depths. In the wave propagation procedure of multiple layers in different depths, the complex amplitude of layers in different depths will gradually diffuse and produce occlusion at another layer. To solve this occlusion problem, a mask is used in the process of layers diffracting. Whether it is forward wave propagation or backward wave propagation of layers, the mask can reduce the occlusion problem between different layers. Otherwise, weighted complex loss function is implemented in the gradient descent optimization process, which analyzes the real part, the imaginary part, and the amplitude part of the focus region between the reconstructed images of the hologram and the target images. The weight parameter is used to adjust the ratio of the amplitude loss of the focus region in the whole loss function. The weight amplitude loss part in weighted complex loss function can decrease the interference of the focus region from the defocus region. The simulations and experiments have validated the effectiveness of the proposed method.
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spelling pubmed-100561742023-03-30 Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction Quan, Jiale Yan, Binbin Sang, Xinzhu Zhong, Chongli Li, Hui Qin, Xiujuan Xiao, Rui Sun, Zhi Dong, Yu Zhang, Huming Micromachines (Basel) Article In this paper, we propose a method to generate multi-depth phase-only holograms using stochastic gradient descent (SGD) algorithm with weighted complex loss function and masked multi-layer diffraction. The 3D scene can be represented by a combination of layers in different depths. In the wave propagation procedure of multiple layers in different depths, the complex amplitude of layers in different depths will gradually diffuse and produce occlusion at another layer. To solve this occlusion problem, a mask is used in the process of layers diffracting. Whether it is forward wave propagation or backward wave propagation of layers, the mask can reduce the occlusion problem between different layers. Otherwise, weighted complex loss function is implemented in the gradient descent optimization process, which analyzes the real part, the imaginary part, and the amplitude part of the focus region between the reconstructed images of the hologram and the target images. The weight parameter is used to adjust the ratio of the amplitude loss of the focus region in the whole loss function. The weight amplitude loss part in weighted complex loss function can decrease the interference of the focus region from the defocus region. The simulations and experiments have validated the effectiveness of the proposed method. MDPI 2023-03-06 /pmc/articles/PMC10056174/ /pubmed/36985013 http://dx.doi.org/10.3390/mi14030605 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Quan, Jiale
Yan, Binbin
Sang, Xinzhu
Zhong, Chongli
Li, Hui
Qin, Xiujuan
Xiao, Rui
Sun, Zhi
Dong, Yu
Zhang, Huming
Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction
title Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction
title_full Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction
title_fullStr Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction
title_full_unstemmed Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction
title_short Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction
title_sort multi-depth computer-generated hologram based on stochastic gradient descent algorithm with weighted complex loss function and masked diffraction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056174/
https://www.ncbi.nlm.nih.gov/pubmed/36985013
http://dx.doi.org/10.3390/mi14030605
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