Raman signal extraction from BCARS intensity measurements using deep learning with a prior excitation profile

Abstract

Broadband Coherent anti-Stokes Raman Scattering (BCARS) microscopy is a useful technique for chemical analysis and allows the full vibrational fingerprint spectrum of a specimen to be obtained in millisec-onds. A major drawback to this technique is the presence of the non-resonant background response producing interference which prevents classical spectral analysis of the sample. Using a convolutional autoencoder and measurements of the laser characteristics, we have shown that it is possible to remove this background with-out requiring supervision, as is typically required for conventional removal methods. This approach therefore simplifies the analysis of hyperspectral images obtained with BCARS.