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Noise Reduction Technique for Raman Spectrum using Deep Learning Network

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Document pages: 6 pages

Abstract: In a normal indoor environment, Raman spectrum encounters noise often concealspectrum peak, leading to difficulty in spectrum interpretation. This paperproposes deep learning (DL) based noise reduction technique for Ramanspectroscopy. The proposed DL network is developed with several training andtest sets of noisy Raman spectrum. The proposed technique is applied to denoiseand compare the performance with different wavelet noise reduction methods.Output signal-to-noise ratio (SNR), root-mean-square error (RMSE) and meanabsolute percentage error (MAPE) are the performance evaluation index. It isshown that output SNR of the proposed noise reduction technology is 10.24 dBgreater than that of the wavelet noise reduction method while the RMSE and theMAPE are 292.63 and 10.09, which are much better than the proposed technique.

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