Denoising Raman spectra using a single layer convolutional model trained on simulated data

Raman spectroscopy is a powerful means of revealing chemical and structural information about a sample and acquiring chemically specific images. Such images often suffer from low signal to noise ratios (SNR). In this report, a novel way to improve the SNR using machine learning tools based on simulated data. The proposed approach offers an alternative to time consuming acquisition and labeling of large data sets and can be readily applied to unknown systems. Here, the efficacy of a single layer denoising network trained only on simulated data was evaluated, and it was found that the proposed model was able to provide a substantial improvement in SNR.