Denoising of stimulated Raman scattering microscopy images via deep learning (Conference Presentation)

Bryce Manifold

doi:10.1117/12.2546376

10 March 2020 Denoising of stimulated Raman scattering microscopy images via deep learning (Conference Presentation)

Bryce Manifold

Proceedings Volume 11252, Advanced Chemical Microscopy for Life Science and Translational Medicine; 1125216 (2020) https://doi.org/10.1117/12.2546376
Event: SPIE BiOS, 2020, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Bryce Manifold "Denoising of stimulated Raman scattering microscopy images via deep learning (Conference Presentation)", Proc. SPIE 11252, Advanced Chemical Microscopy for Life Science and Translational Medicine, 1125216 (10 March 2020); https://doi.org/10.1117/12.2546376

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