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Lensless Computational Imaging Technology Using Deep Convolutional Network
Within the framework of Internet of Things or when constrained in limited space, lensless imaging technology provides effective imaging solutions with low cost and reduced size prototypes. In this paper, we proposed a method combining deep learning with lensless coded mask imaging technology. After...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249064/ https://www.ncbi.nlm.nih.gov/pubmed/32384807 http://dx.doi.org/10.3390/s20092661 |
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author | Chen, Peidong Su, Xiuqin Liu, Muyuan Zhu, Wenhua |
author_facet | Chen, Peidong Su, Xiuqin Liu, Muyuan Zhu, Wenhua |
author_sort | Chen, Peidong |
collection | PubMed |
description | Within the framework of Internet of Things or when constrained in limited space, lensless imaging technology provides effective imaging solutions with low cost and reduced size prototypes. In this paper, we proposed a method combining deep learning with lensless coded mask imaging technology. After replacing lenses with the coded mask and using the inverse matrix optimization method to reconstruct the original scene images, we applied FCN-8s, U-Net, and our modified version of U-Net, which is called Dense-U-Net, for post-processing of reconstructed images. The proposed approach showed supreme performance compared to the classical method, where a deep convolutional network leads to critical improvements of the quality of reconstruction. |
format | Online Article Text |
id | pubmed-7249064 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72490642020-06-10 Lensless Computational Imaging Technology Using Deep Convolutional Network Chen, Peidong Su, Xiuqin Liu, Muyuan Zhu, Wenhua Sensors (Basel) Article Within the framework of Internet of Things or when constrained in limited space, lensless imaging technology provides effective imaging solutions with low cost and reduced size prototypes. In this paper, we proposed a method combining deep learning with lensless coded mask imaging technology. After replacing lenses with the coded mask and using the inverse matrix optimization method to reconstruct the original scene images, we applied FCN-8s, U-Net, and our modified version of U-Net, which is called Dense-U-Net, for post-processing of reconstructed images. The proposed approach showed supreme performance compared to the classical method, where a deep convolutional network leads to critical improvements of the quality of reconstruction. MDPI 2020-05-06 /pmc/articles/PMC7249064/ /pubmed/32384807 http://dx.doi.org/10.3390/s20092661 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 | Article Chen, Peidong Su, Xiuqin Liu, Muyuan Zhu, Wenhua Lensless Computational Imaging Technology Using Deep Convolutional Network |
title | Lensless Computational Imaging Technology Using Deep Convolutional Network |
title_full | Lensless Computational Imaging Technology Using Deep Convolutional Network |
title_fullStr | Lensless Computational Imaging Technology Using Deep Convolutional Network |
title_full_unstemmed | Lensless Computational Imaging Technology Using Deep Convolutional Network |
title_short | Lensless Computational Imaging Technology Using Deep Convolutional Network |
title_sort | lensless computational imaging technology using deep convolutional network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249064/ https://www.ncbi.nlm.nih.gov/pubmed/32384807 http://dx.doi.org/10.3390/s20092661 |
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