SNR enhancement in brillouin microspectroscopy using spectrum reconstruction.

Yuchen Xiang,Peter Török,Matthew R Foreman

doi:10.1364/boe.380798

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.

Highlights

Brillouin spectroscopy is a near century old technique relying on the scattering of incident photons from thermally excited acoustic fluctuations in a medium [1]
Brillouin spectroscopy has seen a renaissance as it has developed from a spectroscopic technique into a more powerful hyper-spectral imaging modality in which mechanical information is mapped with micron level resolution [6,7,8,9,10]
One drawback of using spontaneous Brillouin scattering, is the intrinsically low signal-to-noise ratio (SNR) which in turn leads to relatively long image acquisition times

Summary

Introduction

Brillouin spectroscopy is a near century old technique relying on the scattering of incident photons from thermally excited acoustic fluctuations in a medium (i.e. phonons) [1]. One drawback of using spontaneous Brillouin scattering, is the intrinsically low signal-to-noise ratio (SNR) which in turn leads to relatively long image acquisition times This issue is further exacerbated in fibre-based systems and remains one of the main challenges in designing a clinical Brillouin endoscope device [12]. We restrict our attention to the maximum entropy reconstruction (MER) and wavelet analysis (WA) techniques, due to their applicability in a wide scope of experimental situations, in experiments with low SNR and little knowledge of the sample Note that throughout this work we define the SNR as the ratio of the spectral intensity to the standard deviation of additive noise sources, such as detector noise

Numerical methods

Assessing algorithm performance

Reconstruction of simulated data

Denoising of experimental spectra

Findings

Conclusion