基于深度学习网络的拉曼光谱降噪技术
Noise Reduction Technique for Raman Spectrum using Deep Learning Network
摘要：
在正常的室内环境中，拉曼光谱遇到噪声往往掩盖了光谱峰，导致光谱解释困难。本文提出了一种基于深度学习（DL）的拉曼光谱降噪技术。建议的DL网络开发了几个训练和测试集的噪声拉曼光谱。该技术被应用到去噪和比较不同的小波降噪方法的性能。输出信噪比（SNR）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）是性能评价指标。实验结果表明，该降噪方法的输出信噪比比小波降噪方法高10.24 dB，均方根误差（RMSE）和最大平均误差（MAPE）分别为292.63和10.09，明显优于小波降噪方法。
英文摘要：
In a normal indoor environment, Raman spectrum encounters noise often conceal spectrum peak, leading to difficulty in spectrum interpretation. 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.
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