Adaptive Operator-Based Spectral Deconvolution With the Levenberg-Marquardt Algorithm
Abstract Spectral distortion often occurs in spectral data due to the influence of the bandpass function of the spectrometer. Spectral deconvolution is an effective restoration method to solve this problem. Based on the theory of the maximum posteriori estimation, this paper transforms the spectral...
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Online Access: | https://doi.org/10.1007/s13320-019-0571-8 |
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doaj-b6367d6c8abc42aa8c38d0d81399201e2020-12-06T12:51:51ZengSpringerOpenPhotonic Sensors1674-92512190-74392019-12-0110324225310.1007/s13320-019-0571-8Adaptive Operator-Based Spectral Deconvolution With the Levenberg-Marquardt AlgorithmChan Huang0Feinan Chen1Yuyang Chang2Lin Han3Shuang Li4Jin Hong5Anhui Institute of Optics and Fine Mechanics, Chinese Academy of SciencesAnhui Institute of Optics and Fine Mechanics, Chinese Academy of SciencesAnhui Institute of Optics and Fine Mechanics, Chinese Academy of SciencesAnhui Institute of Optics and Fine Mechanics, Chinese Academy of SciencesAnhui Institute of Optics and Fine Mechanics, Chinese Academy of SciencesAnhui Institute of Optics and Fine Mechanics, Chinese Academy of SciencesAbstract Spectral distortion often occurs in spectral data due to the influence of the bandpass function of the spectrometer. Spectral deconvolution is an effective restoration method to solve this problem. Based on the theory of the maximum posteriori estimation, this paper transforms the spectral deconvolution problem into a multi-parameter optimization problem, and a novel spectral deconvolution method is proposed on the basis of Levenberg-Marquardt algorithm. Furthermore, a spectral adaptive operator is added to the method, which improves the effect of the regularization term. The proposed methods, Richardson-Lucy (R-L) method and Huber-Markov spectroscopic semi-blind deconvolution (HMSBD) method, are employed to deconvolute the white light-emitting diode (LED) spectra with two different color temperatures, respectively. The correction errors, root mean square errors, noise suppression ability, and the computation speed of above methods are compared. The experimental results prove the superiority of the proposed algorithm.https://doi.org/10.1007/s13320-019-0571-8Optical data processingspectrometerdeconvolution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chan Huang Feinan Chen Yuyang Chang Lin Han Shuang Li Jin Hong |
spellingShingle |
Chan Huang Feinan Chen Yuyang Chang Lin Han Shuang Li Jin Hong Adaptive Operator-Based Spectral Deconvolution With the Levenberg-Marquardt Algorithm Photonic Sensors Optical data processing spectrometer deconvolution |
author_facet |
Chan Huang Feinan Chen Yuyang Chang Lin Han Shuang Li Jin Hong |
author_sort |
Chan Huang |
title |
Adaptive Operator-Based Spectral Deconvolution With the Levenberg-Marquardt Algorithm |
title_short |
Adaptive Operator-Based Spectral Deconvolution With the Levenberg-Marquardt Algorithm |
title_full |
Adaptive Operator-Based Spectral Deconvolution With the Levenberg-Marquardt Algorithm |
title_fullStr |
Adaptive Operator-Based Spectral Deconvolution With the Levenberg-Marquardt Algorithm |
title_full_unstemmed |
Adaptive Operator-Based Spectral Deconvolution With the Levenberg-Marquardt Algorithm |
title_sort |
adaptive operator-based spectral deconvolution with the levenberg-marquardt algorithm |
publisher |
SpringerOpen |
series |
Photonic Sensors |
issn |
1674-9251 2190-7439 |
publishDate |
2019-12-01 |
description |
Abstract Spectral distortion often occurs in spectral data due to the influence of the bandpass function of the spectrometer. Spectral deconvolution is an effective restoration method to solve this problem. Based on the theory of the maximum posteriori estimation, this paper transforms the spectral deconvolution problem into a multi-parameter optimization problem, and a novel spectral deconvolution method is proposed on the basis of Levenberg-Marquardt algorithm. Furthermore, a spectral adaptive operator is added to the method, which improves the effect of the regularization term. The proposed methods, Richardson-Lucy (R-L) method and Huber-Markov spectroscopic semi-blind deconvolution (HMSBD) method, are employed to deconvolute the white light-emitting diode (LED) spectra with two different color temperatures, respectively. The correction errors, root mean square errors, noise suppression ability, and the computation speed of above methods are compared. The experimental results prove the superiority of the proposed algorithm. |
topic |
Optical data processing spectrometer deconvolution |
url |
https://doi.org/10.1007/s13320-019-0571-8 |
work_keys_str_mv |
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1724398575661613056 |