Noises investigations and image denoising in femtosecond stimulated Raman scattering microscopy.

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.