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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...

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Detalles Bibliográficos
Autores principales: Chen, Peidong, Su, Xiuqin, Liu, Muyuan, Zhu, Wenhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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.
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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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