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Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network

Compressive sensing (CS) spectroscopy is well known for developing a compact spectrometer which consists of two parts: compressively measuring an input spectrum and recovering the spectrum using reconstruction techniques. Our goal here is to propose a novel residual convolutional neural network (Res...

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Detalles Bibliográficos
Autores principales: Kim, Cheolsun, Park, Dongju, Lee, Heung-No
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7037334/
https://www.ncbi.nlm.nih.gov/pubmed/31973148
http://dx.doi.org/10.3390/s20030594
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author Kim, Cheolsun
Park, Dongju
Lee, Heung-No
author_facet Kim, Cheolsun
Park, Dongju
Lee, Heung-No
author_sort Kim, Cheolsun
collection PubMed
description Compressive sensing (CS) spectroscopy is well known for developing a compact spectrometer which consists of two parts: compressively measuring an input spectrum and recovering the spectrum using reconstruction techniques. Our goal here is to propose a novel residual convolutional neural network (ResCNN) for reconstructing the spectrum from the compressed measurements. The proposed ResCNN comprises learnable layers and a residual connection between the input and the output of these learnable layers. The ResCNN is trained using both synthetic and measured spectral datasets. The results demonstrate that ResCNN shows better spectral recovery performance in terms of average root mean squared errors (RMSEs) and peak signal to noise ratios (PSNRs) than existing approaches such as the sparse recovery methods and the spectral recovery using CNN. Unlike sparse recovery methods, ResCNN does not require a priori knowledge of a sparsifying basis nor prior information on the spectral features of the dataset. Moreover, ResCNN produces stable reconstructions under noisy conditions. Finally, ResCNN is converged faster than CNN.
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spelling pubmed-70373342020-03-11 Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network Kim, Cheolsun Park, Dongju Lee, Heung-No Sensors (Basel) Article Compressive sensing (CS) spectroscopy is well known for developing a compact spectrometer which consists of two parts: compressively measuring an input spectrum and recovering the spectrum using reconstruction techniques. Our goal here is to propose a novel residual convolutional neural network (ResCNN) for reconstructing the spectrum from the compressed measurements. The proposed ResCNN comprises learnable layers and a residual connection between the input and the output of these learnable layers. The ResCNN is trained using both synthetic and measured spectral datasets. The results demonstrate that ResCNN shows better spectral recovery performance in terms of average root mean squared errors (RMSEs) and peak signal to noise ratios (PSNRs) than existing approaches such as the sparse recovery methods and the spectral recovery using CNN. Unlike sparse recovery methods, ResCNN does not require a priori knowledge of a sparsifying basis nor prior information on the spectral features of the dataset. Moreover, ResCNN produces stable reconstructions under noisy conditions. Finally, ResCNN is converged faster than CNN. MDPI 2020-01-21 /pmc/articles/PMC7037334/ /pubmed/31973148 http://dx.doi.org/10.3390/s20030594 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
Kim, Cheolsun
Park, Dongju
Lee, Heung-No
Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network
title Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network
title_full Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network
title_fullStr Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network
title_full_unstemmed Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network
title_short Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network
title_sort compressive sensing spectroscopy using a residual convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7037334/
https://www.ncbi.nlm.nih.gov/pubmed/31973148
http://dx.doi.org/10.3390/s20030594
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