Modified denoising method of Raman spectra-based deep learning for Raman semi-quantitative analysis and imaging

Raman spectroscopic technique is a sensitive and non-destructive technique for structural analysis. However, Raman scattering is an unfavorable process, and the signals are weak, resulting in a low signal-to-noise ratio of spectra, making Raman spectroscopy challenging to gain popularity and implement. As a simple and practical approach, denoising techniques could improve the signal-to-noise ratio, which can help researchers extract information more effectively to reflect the configuration, content, and changes of test samples. However, addressing this issue with traditional algorithms is often challenging due to increased complexity and requirements in Raman spectra denoising. While some deep learning-based approaches enhance the signal-to-noise ratio, obtaining excellent performance models with good generalization is not easy when dealing with real-world tasks. This paper proposes a method of good generalization, robust, and denoising performance by using a convolutional neural network based on a new augmented method and a multi-scale feature extraction fusion block called the multi-scale feature extraction denoising (MFED) model. Specifically, first, we addressed insufficient training data using a new augmented approach through simulation of the Raman data acquisition and, in turn, improved generalization of the MFED model. Subsequently, the mixed Poisson-Gaussian noise model showed commendable robustness when dealing with synthetic and real noise data. Finally, a feature extraction block based on a multi-scale fusion significantly improved denoising effects. The comparison results of different denoising methods demonstrated the good applicability and superiority of the proposed approach. More importantly, the main advantage of the proposed MFED model is that it is easily applicable. We demonstrate that applying MFED as a pre-processing technique for Raman spectra can enhance the prediction accuracy of soybean oil concentration in olive oil. Furthermore, despite the integration time dropping from 3 to 1 s, we still yielded good quality images following MFED model denoising processing on point-scan Raman spectral imaging of cervical cancer cells. The proposed MFED model provides an excellent candidate for increasing the Raman SNR, which can contribute substantially to the application of Raman analytics in research and practices.

Automated summary

Raman spectroscopy is a sensitive and non-destructive technique for structural analysis, but its low signal-to-noise ratio makes it challenging to implement. This paper proposes a MFED model based on a convolutional neural network and multi-scale feature extraction fusion block, which improves the generalization and robustness of the model. The MFED model shows excellent denoising performance using the mixed Poisson-Gaussian noise model and multi-scale feature extraction. The results demonstrate the applicability and superiority of the proposed approach, making it promising for Raman analytics in research and practices.