Image denoising based on Singular-Spectrum-Analysis (SSA) in femtosecond stimulated Raman scattering microscopy

\nStimulated Raman scattering (SRS) microscopy is able to perform high sensitivity biological imaging with high spatial and spectral resolution and image acquisition time of a few seconds. Nevertheless, SRS images often suffer from low SNR, due to the weak Raman cross-section of biomolecules. Therefore, methods aiming to improve image quality are mandatory. In this paper, the performances of a 2D denoising algorithm based on SSA is analysed. SRS imaging of lipids droplets have been used in order to validate our algorithms.\n

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-10-31
DOI
https://doi.org/10.1051/epjconf/202430909004/pdf
Primary Topic
Diverse Scientific and Economic Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Image denoising based on Singular-Spectrum-Analysis (SSA) in femtosecond stimulated Raman scattering microscopy

Springer Link (Chiba Institute of Technology)
Diverse Scientific and Economic Studies
article

Image denoising based on Singular-Spectrum-Analysis (SSA) in femtosecond stimulated Raman scattering microscopy

article en

Abstract

\nStimulated Raman scattering (SRS) microscopy is able to perform high sensitivity biological imaging with high spatial and spectral resolution and image acquisition time of a few seconds. Nevertheless, SRS images often suffer from low SNR, due to the weak Raman cross-section of biomolecules. Therefore, methods aiming to improve image quality are mandatory. In this paper, the performances of a 2D denoising algorithm based on SSA is analysed. SRS imaging of lipids droplets have been used in order to validate our algorithms.\n

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 49%
Diverse Scientific and Economic Studies
DOIOpen Access PDFOpenAlex
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Image denoising based on Singular-Spectrum-Analysis (SSA) in femtosecond stimulated Raman scattering microscopy · Springer Link (Chiba Institute of Technology) (2024) | TGRS Research Map | TGRS