论文标题
使用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
论文作者
论文摘要
在正常的室内环境中，拉曼频谱会遇到噪声通常掩盖频谱峰，从而导致频谱解释难度。本文提出了针对拉曼光谱的基于深度学习（DL）的降噪技术。提出的DL网络是通过多个嘈杂的拉曼光谱的训练和测试集开发的。提出的技术用于Deoise和将性能与不同的小波降噪方法进行比较。输出信噪比（SNR），根平方误差（RMSE）和平均绝对百分比误差（MAPE）是性能评估指数。结果表明，提议的降噪技术的输出SNR比小波降噪方法的输出SNR大10.24 dB，而RMSE和MAPE为292.63和10.09，这比提出的技术要好得多。
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.
