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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...
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/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. |
format | Online Article Text |
id | pubmed-7037334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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