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5 March 2021 Deep learning enabled Raman hyperspectral super-resolution imaging
Conor C. Horgan, Magnus Jensen, Anika Nagelkerke, Jean-Philippe St-Pierre, Tom Vercauteren, Molly Stevens, Mads Bergholt
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Proceedings Volume 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021; 1165514 (2021) https://doi.org/10.1117/12.2577709
Event: SPIE BiOS, 2021, Online Only
Abstract
Spontaneous Raman spectroscopy enables non-ionising, non-destructive, and label-free acquisition of a biochemical fingerprint for a given sample. However, the long integration times required largely prohibit high-throughput applications. Here, we present a comprehensive deep learning framework for extreme speed-up of spontaneous Raman imaging. Our deep learning framework enhances Raman imaging two-fold, effectively reconstructing both spectral and spatial information from low spatial resolution, low signal-to-noise ratio images to achieve extreme Raman imaging time speed-ups of 40-90x while mainting high reconstruction fidelity. As such, our framework could enable a host of higher-throughput spontaneous Raman spectroscopy applications across a diverse range of fields.
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Conor C. Horgan, Magnus Jensen, Anika Nagelkerke, Jean-Philippe St-Pierre, Tom Vercauteren, Molly Stevens, and Mads Bergholt "Deep learning enabled Raman hyperspectral super-resolution imaging", Proc. SPIE 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021, 1165514 (5 March 2021); https://doi.org/10.1117/12.2577709
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KEYWORDS
Raman spectroscopy
Super resolution
Hyperspectral imaging
Imaging spectroscopy
Denoising
Raman scattering
Reconstruction algorithms
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