Noise Reduction Technique for Raman Spectrum using Deep Learning Network

被引：13

作者：

Pan, Liangrui ^{[1
]}

Pipitsunthonsan, Pronthep ^{[1
]}

Zhang, Peng ^{[1
]}

Daengngam, Chalongrat ^{[2
]}

Booranawong, Apidach ^{[1
]}

Chongcheawchamnan, Mitchai ^{[1
]}

机构：

[1] Prince Songkla Univ, Fac Engn, Dept Elect Engn, Hat Yai, Thailand

[2] Prince Songkla Univ, Div Phys Sci, Fac Sci, Hat Yai, Thailand

来源：

2020 13TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2020) | 2020年

关键词：

Deep learning network; wavelet; noise reduction; CNN;

D O I：

10.1109/ISCID51228.2020.00042

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In a normal indoor environment, Raman spectrum encounters noise often conceal spectrum peak, leading to difficulty in spectrum intepretation. This paper proposes deep learning (DL) based noise reduction technique for Raman spectroscopy. The proposed DL network is developed with several training and test sets of noisy Raman spectrum. The proposed technique is applied to denoise and compare the performance with different wavelet noise reduction methods. Output signal-to-noise ratio (SNR), root-mean-square error (RMSE) and mean absolute percentage error (MAPE) are the performance evaluation index. It is shown that output SNR of the proposed noise reduction technology is 10.24 dB greater than that of the wavelet noise reduction method while the RMSE and the MAPE are 292.63 and 10.09, which are much better than the proposed technique.

引用

页码：159 / 163

页数：5

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