Multiframe-based non-local means denoising for Raman spectra.

Area of Science:

• Spectroscopy
• Data Analysis
• Chemical Imaging

Background:

• Raman spectroscopy is a powerful technique for chemical analysis but is often limited by noisy spectra.
• Conventional denoising methods, such as single-frame neighbor pixel filtering, have limitations in effectiveness and robustness.
• There is a need for advanced denoising techniques to improve the quality and reliability of Raman spectral data.

Purpose of the Study:

• To present a novel method for denoising Raman spectra.
• To demonstrate the effectiveness and robustness of the proposed method using simulated and experimental data.
• To compare the performance of the new method against conventional techniques and showcase its application with a cost-effective Raman system.

Main Methods:

• A novel denoising approach based on structure similarity-guided averaging of pixels across the entire signal range and different frames.
• The method extends beyond conventional neighbor pixel-based filtering.
• Fixed denoising parameters were used for consistent evaluation of simulated and experimental Raman datasets.

Main Results:

• The proposed method successfully denoised both simulated and experimental Raman spectra, demonstrating effectiveness and robustness.
• Statistical indicators and visual representations confirmed the quality of the denoised results for simulated data.
• Experimental Raman spectra recovered using a cost-effective waveguide-enhanced Raman spectroscopy system showed high quality comparable to those from expensive conventional Raman microscopes.

Conclusions:

• The developed structure similarity-based averaging method offers a significant advancement in Raman spectra denoising.
• The technique provides a robust and effective solution for improving spectral quality, applicable to various Raman data.
• This method enables high-quality spectral recovery, even with cost-effective instrumentation, broadening the accessibility of Raman spectroscopy.