Noises investigations and image denoising in femtosecond stimulated Raman scattering microscopy

Abstract

In the literature of SRS microscopy, the hardware characterization usually remains separate from the image processing. In this article, we consider both these aspects and statistical properties analysis of image noise, which plays the vital role of joining links between them. Firstly, we perform hardware characterization by systematic measurements of noise sources, demonstrating that our in‐house built microscope is shot noise limited. Secondly, we analyze the statistical properties of the overall image noise, and we prove that the noise distribution can be dependent on image direction, whose origin is the use of a lock‐in time constant longer than pixel dwell time. Finally, we compare the performances of two widespread general algorithms, that is, singular value decomposition and discrete wavelet transform, with a method, that is, singular spectrum analysis (SSA), which has been adapted for stimulated Raman scattering images. In order to validate our algorithms, in our investigations lipids droplets have been used and we demonstrate that the adapted SSA method provides an improvement in image denoising.

Noise Measurements and Noise Statistical Properties Investigations in a Stimulated Raman Scattering Microscope Based on Three Femtoseconds Laser Sources

“…It is worth noting that, in SRS literature, the hypothesis of white noise, generally supposed by many denoising algorithms, is assumed after the shot noise-limited condition has been proven by spectroscopic measurements. We note that this point was raised for the first time in our previous paper [40]. However, here a more complete discussion is provided.…”

“…Still, in the case of SRS, its drawback is that only the C-H region (>2500 cm −1 ) can be explored. This means that the demand, for example, for a biorthogonal platform cannot be accomplished [34][35][36][37][38][39][40].…”

Pinpointing Fat Molecules: Advances in Coherent Raman Scattering Microscopy for Lipid Metabolism

“…To address this challenge, several approaches have been developed. Denoising algorithms , have been widely used to improve the signal-to-noise ratio (SNR) of SRS images. For example, Lin et al developed a spatial-spectral residual learning network to enhance the SNR of fingerprint region SRS images.…”

Stimulated Raman Scattering Microscopy: A Review

“…It is worth noting that due to the weak Raman cross-section of biomolecules, SRS microscopy often suffers from a low signal-to-noise ratio (SNR), and SRS applications in biological/biomedical imaging could be compromised. In the literature on SRS, noise investigations are often overlooked, even if the signal-to-noise ratio (SNR) is crucial for spectroscopy [36,37] and biological imaging [38][39][40][41][42][43][44][45]. A safer way would be to evaluate both the stimulated Raman loss and the stimulated Raman gain, as these two contributions should be equal in the absence of artifacts.…”