SNR Enhancement in Brillouin Microspectroscopy Using Spectrum Reconstruction

Overview

Journal Biomed Opt Express

Specialty Radiology

Date 2020 Mar 6

PMID 32133235

Citations 5

Authors

Yuchen Xiang

Matthew R Foreman

Peter Torok

Affiliations

Soon will be listed here.

Abstract

Brillouin spectroscopy can suffer from low signal-to-noise ratios (SNRs). Such low SNRs can render common data analysis protocols unreliable, especially for SNRs below ∼10. 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 Cramér-Rao lower bound. Superior estimation results are demonstrated even at low SNRs (≥ 1). Denoising is 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 ±1% at unity SNR.

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