Denoising Raman spectra using fully convolutional encoder–decoder network. (22nd May 2022) - Loc, Irem
Abstract: Raman spectroscopy is a vibrational method that gives molecular information rapidly and non‐invasively. Despite its advantages, the weak intensity of Raman spectroscopy leads to low‐quality signals, particularly with tissue samples. The requirement of high exposure times makes Raman a time‐consuming process and diminishes its non‐invasive property while studying living tissues. Novel denoising techniques using convolutional neural networks (CNN) have achieved remarkable results in image processing. Here, we propose a similar approach for noise reduction for the Raman spectra acquired with 10 × lower exposure times. In this work, we developed fully convolutional encoder‐decoder architecture (FCED) and trained them with noisy Raman signals. The results demonstrate that our model is superior ( p value < 0.0001) to the conventional denoising techniques such as the Savitzky‐Golay filter and wavelet denoising. Improvement in the signal‐to‐noise ratio values ranges from 20 % to 80 %, depending on the initial signal‐to‐noise ratio. Thus, we proved that tissue analysis could be done in a shorter time without any need for instrumental enhancement. Abstract : The weak intensity of the Raman spectroscopy produces a low‐quality signal when working with the tissue samples. We proposed a deep learning approach to improve Raman spectra taken with short exposure times. We showed that our method is able to improve the signal‐to‐noise ratio up to 80% on low‐exposure Raman spectra.