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Locating and Imaging through Scattering Medium in a Large Depth

Scattering medium brings great difficulties to locate and reconstruct objects especially when the objects are distributed in different positions. In this paper, a novel physics and learning-heuristic method is presented to locate and image the object through a strong scattering medium. A novel physi...

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Detalles Bibliográficos
Autores principales: Zhu, Shuo, Guo, Enlai, Cui, Qianying, Bai, Lianfa, Han, Jing, Zheng, Dongliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796240/
https://www.ncbi.nlm.nih.gov/pubmed/33375637
http://dx.doi.org/10.3390/s21010090
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author Zhu, Shuo
Guo, Enlai
Cui, Qianying
Bai, Lianfa
Han, Jing
Zheng, Dongliang
author_facet Zhu, Shuo
Guo, Enlai
Cui, Qianying
Bai, Lianfa
Han, Jing
Zheng, Dongliang
author_sort Zhu, Shuo
collection PubMed
description Scattering medium brings great difficulties to locate and reconstruct objects especially when the objects are distributed in different positions. In this paper, a novel physics and learning-heuristic method is presented to locate and image the object through a strong scattering medium. A novel physics-informed framework, named DINet, is constructed to predict the depth and the image of the hidden object from the captured speckle pattern. With the phase-space constraint and the efficient network structure, the proposed method enables to locate the object with a depth mean error less than 0.05 mm, and image the object with an average peak signal-to-noise ratio (PSNR) above 24 dB, ranging from 350 mm to 1150 mm. The constructed DINet firstly solves the problem of quantitative locating and imaging via a single speckle pattern in a large depth. Comparing with the traditional methods, it paves the way to the practical applications requiring multi-physics through scattering media.
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spelling pubmed-77962402021-01-10 Locating and Imaging through Scattering Medium in a Large Depth Zhu, Shuo Guo, Enlai Cui, Qianying Bai, Lianfa Han, Jing Zheng, Dongliang Sensors (Basel) Letter Scattering medium brings great difficulties to locate and reconstruct objects especially when the objects are distributed in different positions. In this paper, a novel physics and learning-heuristic method is presented to locate and image the object through a strong scattering medium. A novel physics-informed framework, named DINet, is constructed to predict the depth and the image of the hidden object from the captured speckle pattern. With the phase-space constraint and the efficient network structure, the proposed method enables to locate the object with a depth mean error less than 0.05 mm, and image the object with an average peak signal-to-noise ratio (PSNR) above 24 dB, ranging from 350 mm to 1150 mm. The constructed DINet firstly solves the problem of quantitative locating and imaging via a single speckle pattern in a large depth. Comparing with the traditional methods, it paves the way to the practical applications requiring multi-physics through scattering media. MDPI 2020-12-25 /pmc/articles/PMC7796240/ /pubmed/33375637 http://dx.doi.org/10.3390/s21010090 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Letter
Zhu, Shuo
Guo, Enlai
Cui, Qianying
Bai, Lianfa
Han, Jing
Zheng, Dongliang
Locating and Imaging through Scattering Medium in a Large Depth
title Locating and Imaging through Scattering Medium in a Large Depth
title_full Locating and Imaging through Scattering Medium in a Large Depth
title_fullStr Locating and Imaging through Scattering Medium in a Large Depth
title_full_unstemmed Locating and Imaging through Scattering Medium in a Large Depth
title_short Locating and Imaging through Scattering Medium in a Large Depth
title_sort locating and imaging through scattering medium in a large depth
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796240/
https://www.ncbi.nlm.nih.gov/pubmed/33375637
http://dx.doi.org/10.3390/s21010090
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