Denoising Autoencoder Aided Spectrum Reconstruction for Colloidal Quantum Dot Spectrometers

Jinhui Zhang; Xueyu Zhu; Jie Bao

doi:10.1109/JSEN.2020.3039973

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Denoising Autoencoder Aided Spectrum Reconstruction for Colloidal Quantum Dot Spectrometers

Journal article

Peer reviewed

Denoising Autoencoder Aided Spectrum Reconstruction for Colloidal Quantum Dot Spectrometers

Jinhui Zhang, Xueyu Zhu and Jie Bao

IEEE sensors journal, Vol.21(5), pp.6450-6458

03/01/2021

DOI: 10.1109/JSEN.2020.3039973

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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.

denoising autoencoder

Image reconstruction

Miniature spectrometer

Noise measurement

Noise reduction

Quantum dots

Reconstruction algorithms

Sensitivity

Sensors

spectrum reconstruction

Details

Title: Subtitle: Denoising Autoencoder Aided Spectrum Reconstruction for Colloidal Quantum Dot Spectrometers
Creators: Jinhui Zhang - Tsinghua University
Xueyu Zhu - University of Iowa
Jie Bao - Tsinghua University
Resource Type: Journal article
Publication Details: IEEE sensors journal, Vol.21(5), pp.6450-6458
Publisher: IEEE
DOI: 10.1109/JSEN.2020.3039973
ISSN: 1530-437X
eISSN: 1558-1748
Grant note: 504054 / Simons Foundation (10.13039/100000893) BNR2019ZS01005 / Beijing National Research Center for Information Science and Technology (10.13039/501100017582) Beijing Innovation Center for Future Chips, Tsinghua University (10.13039/501100012282)
Language: English
Date published: 03/01/2021
Academic Unit: Mathematics
Record Identifier: 9984241157102771

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