Convolutional neural network-based retrieval of Raman signals from CARS spectra

Abstract

We report the studies on the automatic extraction of the Raman signal from coherent anti-Stokes Raman scattering (CARS) spectra by using a convolutional neural network (CNN) model. The model architecture is adapted from literature and retrained with synthetic and semi-synthetic data. The synthesized CARS spectra better approximate the experimental CARS spectra. The retrained model accurately predicts spectral lines throughout the spectral range, even with minute intensities, which demonstrates the potential of the model. Further, the extracted Raman line-shapes are in good agreement with the original ones, with an RMS error of less than 7% on average and have shown correlation coefficients of more than 0.9. Finally, this approach has a strong potential in accurately estimating Raman signals from complex CARS data for various applications.