1. Deep learning enabled Raman hyperspectral super-resolution imaging
- Author
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Conor C. Horgan, Tom Vercauteren, Magnus Jensen, Jean-Philippe St-Pierre, Anika Nagelkerke, Molly M. Stevens, and Mads Sylvest Bergholt
- Subjects
Materials science ,Artificial neural network ,business.industry ,Noise reduction ,Deep learning ,Hyperspectral imaging ,Superresolution ,symbols.namesake ,symbols ,Artificial intelligence ,Raman spectroscopy ,business ,Spatial analysis ,Image resolution ,Remote sensing - 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.
- Published
- 2021
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