Simulation-Driven Deep Learning Framework for Raman Spectral Denoising Under Fluorescence-Dominant Conditions
This work addresses a domain-specific challenge in biomedical Raman spectroscopy for tissue analysis, presenting an incremental improvement by combining existing noise modeling with deep learning.
The study tackled the problem of weak Raman scattering and strong fluorescence background degrading signal quality in biomedical diagnostics by developing a simulation-driven deep learning framework for denoising Raman spectra, demonstrating improved spectral quality for faster and more accurate tissue analysis.
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.