Iterative differential autoregressive spectrum estimation for Raman spectrum denoising

Although ultraviolet (UV) laser Raman spectroscopy offers the benefits of stronger signals, partial separation fluorescence and spectra, increased eye safety, it suffers from excessive noise, poor resolution, low maturity level, small intensities remotely acquired signals therefore needs to be used in combination with effective denoising techniques. Herein, a approach denoted as iterative differential autoregressive spectrum estimation was developed relying on assumption that more detailed peaks can obtained by dividing into multiple layers different intensity levels estimating energy distribution each layer. Specifically, layer computed difference between upper its model spectrum, at progressively lower considered. Compared traditional techniques, our method exhibited good noise suppression performance an excellent peak restoration ability while offering advantages decreased spectral resolution loss stable robustness. Cutoff optimization strategies were proposed improve convergence thus decrease calculation time 0.18 s meet remote spectrometers for real-time under condition long integration. The technique paves way based power estimation, has strong adaptive potential, extended other applications.