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Proceedings Paper

Denoising of stimulated Raman scattering microscopy images via deep learning (Conference Presentation)
Author(s): Bryce Manifold

Paper Abstract

Stimulated Raman scattering (SRS) images often suffer from low signal to noise ratio (SNR) due to absorption and scattering of light as well as limited optical power. We use deep learning to significantly improve the SNR of SRS images. Our algorithm, based on a U-Net convolutional neural network, significantly outperforms existing denoising algorithms. The trained denoising algorithm is applicable to images acquired at different imaging powers, depths, and experimental geometries not explicitly included in the training. Our results identify potential towards in vivo applications, where ground-truth images are not always available to create a paired training set for supervised learning.

Paper Details

Date Published: 10 March 2020
Proc. SPIE 11252, Advanced Chemical Microscopy for Life Science and Translational Medicine, 1125216 (10 March 2020); doi: 10.1117/12.2546376
Show Author Affiliations
Bryce Manifold, Univ. of Washington (United States)

Published in SPIE Proceedings Vol. 11252:
Advanced Chemical Microscopy for Life Science and Translational Medicine
Ji-Xin Cheng; Wei Min; Garth J. Simpson, Editor(s)

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