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Ghost edge detection based on HED network

In this paper, we present an edge detection scheme based on ghost imaging (GI) with a holistically-nested neural network. The so-called holistically-nested edge detection (HED) network is adopted to combine the fully convolutional neural network (CNN) with deep supervision to learn image edges effec...

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Autores principales: Zhao, Shengmei, Cui, Yifang, He, Xing, Wang, Le
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Higher Education Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756244/
https://www.ncbi.nlm.nih.gov/pubmed/36637672
http://dx.doi.org/10.1007/s12200-022-00036-1
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author Zhao, Shengmei
Cui, Yifang
He, Xing
Wang, Le
author_facet Zhao, Shengmei
Cui, Yifang
He, Xing
Wang, Le
author_sort Zhao, Shengmei
collection PubMed
description In this paper, we present an edge detection scheme based on ghost imaging (GI) with a holistically-nested neural network. The so-called holistically-nested edge detection (HED) network is adopted to combine the fully convolutional neural network (CNN) with deep supervision to learn image edges effectively. Simulated data are used to train the HED network, and the unknown object’s edge information is reconstructed from the experimental data. The experiment results show that, when the compression ratio (CR) is 12.5%, this scheme can obtain a high-quality edge information with a sub-Nyquist sampling ratio and has a better performance than those using speckle-shifting GI (SSGI), compressed ghost edge imaging (CGEI) and subpixel-shifted GI (SPSGI). Indeed, the proposed scheme can have a good signal-to-noise ratio performance even if the sub-Nyquist sampling ratio is greater than 5.45%. Since the HED network is trained by numerical simulations before the experiment, this proposed method provides a promising way for achieving edge detection with small measurement times and low time cost. GRAPHICAL ABSTRACT: [Image: see text]
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spelling pubmed-97562442023-01-06 Ghost edge detection based on HED network Zhao, Shengmei Cui, Yifang He, Xing Wang, Le Front Optoelectron Research Article In this paper, we present an edge detection scheme based on ghost imaging (GI) with a holistically-nested neural network. The so-called holistically-nested edge detection (HED) network is adopted to combine the fully convolutional neural network (CNN) with deep supervision to learn image edges effectively. Simulated data are used to train the HED network, and the unknown object’s edge information is reconstructed from the experimental data. The experiment results show that, when the compression ratio (CR) is 12.5%, this scheme can obtain a high-quality edge information with a sub-Nyquist sampling ratio and has a better performance than those using speckle-shifting GI (SSGI), compressed ghost edge imaging (CGEI) and subpixel-shifted GI (SPSGI). Indeed, the proposed scheme can have a good signal-to-noise ratio performance even if the sub-Nyquist sampling ratio is greater than 5.45%. Since the HED network is trained by numerical simulations before the experiment, this proposed method provides a promising way for achieving edge detection with small measurement times and low time cost. GRAPHICAL ABSTRACT: [Image: see text] Higher Education Press 2022-08-03 /pmc/articles/PMC9756244/ /pubmed/36637672 http://dx.doi.org/10.1007/s12200-022-00036-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Zhao, Shengmei
Cui, Yifang
He, Xing
Wang, Le
Ghost edge detection based on HED network
title Ghost edge detection based on HED network
title_full Ghost edge detection based on HED network
title_fullStr Ghost edge detection based on HED network
title_full_unstemmed Ghost edge detection based on HED network
title_short Ghost edge detection based on HED network
title_sort ghost edge detection based on hed network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756244/
https://www.ncbi.nlm.nih.gov/pubmed/36637672
http://dx.doi.org/10.1007/s12200-022-00036-1
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