VECTOR: Very deep convolutional autoencoders for non-resonant background removal in broadband coherent anti-Stokes Raman scattering

Rapid label-free spectroscopy of biological and chemical specimen via molecular vibration through means of broadband coherent anti-Stokes Raman scattering (B-CARS) could serve as a basis for a robust diagnostic platform for a wide range of applications. A limiting factor of CARS is the presence of a non-resonant background (NRB) signal, endemic to the technique. This background is multiplicative with the chemically resonant signal, meaning the perturbation it generates cannot be accounted for simply. Although several numerical approaches exist to account for and remove the NRB, they generally require some estimate of the NRB in the form of a separate measurement. In this paper, we propose a deep neural network architecture called Very dEep Convolutional auTOencodeRs (VECTOR), which retrieves the analytical Raman-like spectrum from CARS spectra through training of simulated noisy CARS spectra, without the need for an NRB reference measurement. VECTOR is composed of an encoder and a decoder. The encoder aims to compress the input to a lower dimensional latent representation without losing critical information. The decoder learns to reconstruct the input from the compressed representation. We also introduce skip connection that bypass from the encoder to the decoder, which benefits the reconstruction performance for deeper networks. We conduct abundant experiments to compare our proposed VECTOR to previous approaches in the literature, including the widely applied Kramers-Kronig method, as well as two another recently proposed methods that also use neural networks.