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Far-field super-resolution ghost imaging with a deep neural network constraint
Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8720314/ https://www.ncbi.nlm.nih.gov/pubmed/34974515 http://dx.doi.org/10.1038/s41377-021-00680-w |
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author | Wang, Fei Wang, Chenglong Chen, Mingliang Gong, Wenlin Zhang, Yu Han, Shensheng Situ, Guohai |
author_facet | Wang, Fei Wang, Chenglong Chen, Mingliang Gong, Wenlin Zhang, Yu Han, Shensheng Situ, Guohai |
author_sort | Wang, Fei |
collection | PubMed |
description | Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone, and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications. |
format | Online Article Text |
id | pubmed-8720314 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87203142022-01-13 Far-field super-resolution ghost imaging with a deep neural network constraint Wang, Fei Wang, Chenglong Chen, Mingliang Gong, Wenlin Zhang, Yu Han, Shensheng Situ, Guohai Light Sci Appl Article Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone, and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications. Nature Publishing Group UK 2022-01-01 /pmc/articles/PMC8720314/ /pubmed/34974515 http://dx.doi.org/10.1038/s41377-021-00680-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wang, Fei Wang, Chenglong Chen, Mingliang Gong, Wenlin Zhang, Yu Han, Shensheng Situ, Guohai Far-field super-resolution ghost imaging with a deep neural network constraint |
title | Far-field super-resolution ghost imaging with a deep neural network constraint |
title_full | Far-field super-resolution ghost imaging with a deep neural network constraint |
title_fullStr | Far-field super-resolution ghost imaging with a deep neural network constraint |
title_full_unstemmed | Far-field super-resolution ghost imaging with a deep neural network constraint |
title_short | Far-field super-resolution ghost imaging with a deep neural network constraint |
title_sort | far-field super-resolution ghost imaging with a deep neural network constraint |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8720314/ https://www.ncbi.nlm.nih.gov/pubmed/34974515 http://dx.doi.org/10.1038/s41377-021-00680-w |
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