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Open AccessArticle

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning

1
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(2), 644; https://doi.org/10.3390/s18020644
Received: 5 January 2018 / Revised: 29 January 2018 / Accepted: 13 February 2018 / Published: 22 February 2018
(This article belongs to the Special Issue Spectroscopy Based Sensors)
The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer. View Full-Text
Keywords: filter-based miniature spectrometer; spectral reconstruction; sparse optimization; dictionary learning filter-based miniature spectrometer; spectral reconstruction; sparse optimization; dictionary learning
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Zhang, S.; Dong, Y.; Fu, H.; Huang, S.-L.; Zhang, L. A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning. Sensors 2018, 18, 644.

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