Denoising Stimulated Raman Spectroscopic Images by Total Variation Minimization

High-speed coherent Raman scattering imaging is opening a new avenue to unveil cellular machinery by visualizing the spatiotemporal dynamics of target molecules or intracellular organelles. By extracting signals from the laser at megahertz modulation frequency, current stimulated Raman scattering (SRS) microscopy has reached shot-noise-limited detection sensitivity. The laser-based local oscillator in SRS microscopy not only generates high levels of signal but also delivers a large shot noise that degrades image quality and spectral fidelity. Here, we demonstrate a denoising algorithm that removes the noise in both spatial and spectral domains by total variation minimization. The signal-to-noise ratio of SRS spectroscopic images was improved by up to 57 times for diluted dimethyl sulfoxide solutions and by 15 times for biological tissues. Weak Raman peaks of target molecules originally buried in the noise were unraveled. Coupling the denoising algorithm with multivariate curve resolution allowed discrimination of fat stores from protein-rich organelles in Caenorhabditis elegans. Together, our method significantly improved detection sensitivity without frame averaging, which can be useful for in vivo spectroscopic imaging.