拉曼光谱
卷积神经网络
分光计
谱线
噪音(视频)
相干反斯托克斯拉曼光谱
人工智能
最大后验估计
模式识别(心理学)
先验与后验
拉曼散射
光学
分析化学(期刊)
计算机科学
材料科学
化学
物理
数学
统计
哲学
最大似然
认识论
色谱法
天文
图像(数学)
作者
Qian Zhou,Zhiyong Zou,Han Lin
出处
期刊:Coatings
[Multidisciplinary Digital Publishing Institute]
日期:2022-08-22
卷期号:12 (8): 1229-1229
被引量:2
标识
DOI:10.3390/coatings12081229
摘要
Raman spectroscopy, measured by a Raman spectrometer, is usually disturbed by the instrument response function and noise, which leads to certain measurement error and further affects the accuracy of substance identification. In this paper, we propose a spectral reconstruction method which combines the existing maximum a posteriori (MAP) method and deep learning (DL) to recover the degraded Raman spectrum. The proposed method first employs the MAP method to reconstruct the measured Raman spectra, so as to obtain preliminary estimated Raman spectra. Then, a convolutional neural network (CNN) is trained by using the preliminary estimated Raman spectra and the real Raman spectra to learn the mapping from the preliminary estimated Raman spectra to the real Raman spectra, so as to achieve a better spectral reconstruction effect than merely using the MAP method or a CNN. To prove the effectiveness of the proposed spectral reconstruction method, we employed the proposed method and some traditional spectral reconstruction methods to reconstruct the simulated and measured Raman spectra, respectively. The experimental results show that compared with traditional methods, the estimated Raman spectra reconstructed by the proposed method are closer to the real Raman spectra.
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