Denoising Autoencoder Aided Spectrum Reconstruction for Colloidal Quantum Dot Spectrometers

Recently, the colloidal quantum dot spectrometer has received much attention due to its advantages in cost, size, and operation. Yet, just like many other filter-based miniature spectrometers, spectrum reconstruction for the colloidal quantum dot spectrometer is typically prone to the measurement no...

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Published in:	IEEE sensors journal Vol. 21; no. 5; pp. 6450 - 6458
Main Authors:	Zhang, Jinhui, Zhu, Xueyu, Bao, Jie
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Colloids Correlation analysis Datasets denoising autoencoder Image reconstruction Machine learning Miniature spectrometer Noise Noise measurement Noise reduction Quantum dots Reconstruction Reconstruction algorithms Sensitivity Sensors Spectrometers spectrum reconstruction
ISSN:	1530-437X, 1558-1748
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Abstract	Recently, the colloidal quantum dot spectrometer has received much attention due to its advantages in cost, size, and operation. Yet, just like many other filter-based miniature spectrometers, spectrum reconstruction for the colloidal quantum dot spectrometer is typically prone to the measurement noise due to the correlation of the filters. In this paper, we propose an effective spectrum reconstruction method for the colloidal quantum dot spectrometer, which can recover high-quality spectra in noisy environments. Specifically, we employ a denoising autoencoder, a machine-learning approach, to reduce noise in the filters' raw measurements before performing the reconstruction. After that, we reconstruct the spectra with the denoised data by a sparse recovery algorithm. We investigate the feasibility of the proposed reconstruction approach on a synthetic dataset and an experimental dataset collected by the colloidal quantum dot spectrometer. The results demonstrate that the proposed approach could deliver accurate reconstruction results even when data are corrupted with the measurement noise.
AbstractList	Recently, the colloidal quantum dot spectrometer has received much attention due to its advantages in cost, size, and operation. Yet, just like many other filter-based miniature spectrometers, spectrum reconstruction for the colloidal quantum dot spectrometer is typically prone to the measurement noise due to the correlation of the filters. In this paper, we propose an effective spectrum reconstruction method for the colloidal quantum dot spectrometer, which can recover high-quality spectra in noisy environments. Specifically, we employ a denoising autoencoder, a machine-learning approach, to reduce noise in the filters' raw measurements before performing the reconstruction. After that, we reconstruct the spectra with the denoised data by a sparse recovery algorithm. We investigate the feasibility of the proposed reconstruction approach on a synthetic dataset and an experimental dataset collected by the colloidal quantum dot spectrometer. The results demonstrate that the proposed approach could deliver accurate reconstruction results even when data are corrupted with the measurement noise.
Author	Zhu, Xueyu Bao, Jie Zhang, Jinhui
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References	ref13 (ref34) 2019 ref15 chang (ref11) 2010 ref14 ref31 ref30 ref33 ref32 ref10 ref2 ref1 (ref39) 0 ref17 ref16 kim (ref35) 2020; 20 ref19 ref18 xu (ref29) 2015 lu (ref20) 2018; 10665 (ref37) 2020 ref24 ref23 ref26 chang (ref12) 2010 ref22 ref21 ref28 kingma (ref38) 2014 ref8 ref7 ref9 ref4 (ref36) 2020 ref3 ref6 ref5 (ref27) 0 xie (ref25) 2012
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SubjectTerms	Algorithms Colloids Correlation analysis Datasets denoising autoencoder Image reconstruction Machine learning Miniature spectrometer Noise Noise measurement Noise reduction Quantum dots Reconstruction Reconstruction algorithms Sensitivity Sensors Spectrometers spectrum reconstruction
Title	Denoising Autoencoder Aided Spectrum Reconstruction for Colloidal Quantum Dot Spectrometers
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