SNR Enhancement in Brillouin Microspectroscopy using Spectrum Reconstruction

TL;DR

This paper introduces spectrum reconstruction algorithms to enhance SNR in Brillouin microspectroscopy, enabling more accurate spectral analysis even at very low SNR levels, validated through simulations and experiments.

Contribution

It presents the application of maximum entropy reconstruction and wavelet analysis for denoising Brillouin spectra, improving shift and linewidth estimation accuracy at low SNRs.

Findings

Superior estimation accuracy at SNRs as low as 1

Denoising enables precise measurement of water's speed of sound

Experimental results match theoretical predictions within 1%

Abstract

Brillouin imaging suffers from intrinsically low signal-to-noise ratios (SNR). Such low SNRs can render common data analysis protocols unreliable, especially for SNRs below $\sim10$. In this work we exploit two denoising algorithms, namely maximum entropy reconstruction (MER) and wavelet analysis (WA), to improve the accuracy and precision in determination of Brillouin shifts and linewidth. Algorithm performance is quantified using Monte-Carlo simulations and benchmarked against the Cram\'er-Rao lower bound. Superior estimation results are demonstrated even at low SNRS ($\geq 1$). Denoising was furthermore applied to experimental Brillouin spectra of distilled water at room temperature, allowing the speed of sound in water to be extracted. Experimental and theoretical values were found to be consistent to within $\pm1\%$ at unity SNR.