Simulation-Driven Deep Learning Framework for Raman Spectral Denoising Under Fluorescence-Dominant Conditions

TL;DR

This paper introduces a simulation-driven deep learning framework that effectively denoises Raman spectra affected by fluorescence, enhancing biomedical tissue analysis accuracy and speed.

Contribution

It presents a novel physics-informed deep learning approach that models noise sources and trains on realistic simulated spectra for improved denoising under fluorescence interference.

Findings

Enhanced spectral quality in fluorescence-dominated conditions

Improved accuracy and speed of tissue analysis

Potential for real-time biomedical diagnostics

Abstract

Raman spectroscopy enables non-destructive, label-free molecular analysis with high specificity, making it a powerful tool for biomedical diagnostics. However, its application to biological tissues is challenged by inherently weak Raman scattering and strong fluorescence background, which significantly degrade signal quality. In this study, we present a simulation-driven denoising framework that combines a statistically grounded noise model with deep learning to enhance Raman spectra acquired under fluorescence-dominated conditions. We comprehensively modeled major noise sources. Based on this model, we generated biologically realistic Raman spectra and used them to train a cascaded deep neural network designed to jointly suppress stochastic detector noise and fluorescence baseline interference. To evaluate the performance of our approach, we simulated human skin spectra derived from real experimental data as a validation case study. Our results demonstrate the potential of physics-informed learning to improve spectral quality and enable faster, more accurate Raman-based tissue analysis.