Noise Reduction Technique for Raman Spectrum using Deep Learning Network

TL;DR

This paper introduces a deep learning-based noise reduction method for Raman spectra, significantly improving signal clarity over traditional wavelet methods in indoor environments.

Contribution

A novel deep learning network for denoising Raman spectra, outperforming wavelet noise reduction techniques in key performance metrics.

Findings

SNR improved by 10.24 dB over wavelet methods

RMSE and MAPE significantly lower than existing methods

Effective noise reduction in indoor Raman spectroscopy environments

Abstract

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.