Noise Reduction Technique for Raman Spectrum using Deep Learning Network
This addresses noise interference in Raman spectrum interpretation for analytical chemistry applications, but appears incremental as it builds on existing denoising methods.
The paper tackles noise reduction in Raman spectroscopy by proposing a deep learning-based technique, achieving a 10.24 dB higher output SNR compared to wavelet methods, with RMSE and MAPE values of 292.63 and 10.09 respectively.
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.