Raman spectroscopy enhancement based on deep learning

Raman spectroscopy is an important analytical tool, but the weak intensity of Raman signals caused by Stokes shifts limits its practical applications. To enhance Raman signals, techniques such as Surface-Enhanced Raman Spectroscopy (SERS) and Stimulated Raman Scattering (SRS) are commonly used, but these methods rely on external enhancement agents and complex equipment. In this study, we propose a novel deep learning-based method that can directly enhance Raman signals without dependence on external agents or complicated setups. Experimental validation using Rhodamine 6G and Crystal Violet demonstrated significant signal enhancement. To verify the accuracy of the enhancement, we calculated the Root Mean Square Error (RMSE) and Normalized Mean Square Error (NMSE) for Rhodamine 6G, which were 534.2 and 0.21%, respectively, achieving a 209.1-fold signal enhancement. This approach demonstrates potential for directly amplifying weak Raman signals, offering new insights for advancing Raman spectroscopy applications across various fields.