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Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning
Underwater ghost imaging based on deep learning can effectively reduce the influence of forward scattering and back scattering of water. With the help of data-driven methods, high-quality results can be reconstructed. However, the training of the underwater ghost imaging requires enormous paired und...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412451/ https://www.ncbi.nlm.nih.gov/pubmed/36015921 http://dx.doi.org/10.3390/s22166161 |
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author | Yang, Xu Yu, Zhongyang Jiang, Pengfei Xu, Lu Hu, Jiemin Wu, Long Zou, Bo Zhang, Yong Zhang, Jianlong |
author_facet | Yang, Xu Yu, Zhongyang Jiang, Pengfei Xu, Lu Hu, Jiemin Wu, Long Zou, Bo Zhang, Yong Zhang, Jianlong |
author_sort | Yang, Xu |
collection | PubMed |
description | Underwater ghost imaging based on deep learning can effectively reduce the influence of forward scattering and back scattering of water. With the help of data-driven methods, high-quality results can be reconstructed. However, the training of the underwater ghost imaging requires enormous paired underwater datasets, which are difficult to obtain directly. Although the Cycle-GAN method solves the problem to some extent, the blurring degree of the fuzzy class of the paired underwater datasets generated by Cycle-GAN is relatively unitary. To solve this problem, a few-shot underwater image generative network method is proposed. Utilizing the proposed few-shot learning image generative method, the generated paired underwater datasets are better than those obtained by the Cycle-GAN method, especially under the condition of few real underwater datasets. In addition, to reconstruct high-quality results, an underwater deblurring ghost imaging method is proposed. The reconstruction method consists of two parts: reconstruction and deblurring. The experimental and simulation results show that the proposed reconstruction method has better performance in deblurring at a low sampling rate, compared with existing underwater ghost imaging methods based on deep learning. The proposed reconstruction method can effectively increase the clarity degree of the underwater reconstruction target at a low sampling rate and promotes the further applications of underwater ghost imaging. |
format | Online Article Text |
id | pubmed-9412451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94124512022-08-27 Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning Yang, Xu Yu, Zhongyang Jiang, Pengfei Xu, Lu Hu, Jiemin Wu, Long Zou, Bo Zhang, Yong Zhang, Jianlong Sensors (Basel) Article Underwater ghost imaging based on deep learning can effectively reduce the influence of forward scattering and back scattering of water. With the help of data-driven methods, high-quality results can be reconstructed. However, the training of the underwater ghost imaging requires enormous paired underwater datasets, which are difficult to obtain directly. Although the Cycle-GAN method solves the problem to some extent, the blurring degree of the fuzzy class of the paired underwater datasets generated by Cycle-GAN is relatively unitary. To solve this problem, a few-shot underwater image generative network method is proposed. Utilizing the proposed few-shot learning image generative method, the generated paired underwater datasets are better than those obtained by the Cycle-GAN method, especially under the condition of few real underwater datasets. In addition, to reconstruct high-quality results, an underwater deblurring ghost imaging method is proposed. The reconstruction method consists of two parts: reconstruction and deblurring. The experimental and simulation results show that the proposed reconstruction method has better performance in deblurring at a low sampling rate, compared with existing underwater ghost imaging methods based on deep learning. The proposed reconstruction method can effectively increase the clarity degree of the underwater reconstruction target at a low sampling rate and promotes the further applications of underwater ghost imaging. MDPI 2022-08-17 /pmc/articles/PMC9412451/ /pubmed/36015921 http://dx.doi.org/10.3390/s22166161 Text en © 2022 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 Yang, Xu Yu, Zhongyang Jiang, Pengfei Xu, Lu Hu, Jiemin Wu, Long Zou, Bo Zhang, Yong Zhang, Jianlong Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning |
title | Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning |
title_full | Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning |
title_fullStr | Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning |
title_full_unstemmed | Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning |
title_short | Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning |
title_sort | deblurring ghost imaging reconstruction based on underwater dataset generated by few-shot learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412451/ https://www.ncbi.nlm.nih.gov/pubmed/36015921 http://dx.doi.org/10.3390/s22166161 |
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