期刊
OPTICS EXPRESS
卷 28, 期 14, 页码 20422-20437出版社
OPTICAL SOC AMER
DOI: 10.1364/OE.397606
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资金
- Intramural NIST DOC [9999-NIST] Funding Source: Medline
We present a new collection of processing techniques, collectively factorized Kramers-Kronig and error correction (fKK-EC), for (a) Raman signal extraction, (b) denoising, and (c) phase- and scale-error correction in coherent anti-Stokes Raman scattering (CARS) hyperspectral imaging and spectroscopy. These new methods are orders-of-magnitude faster than conventional methods and are capable of real-time performance, owing to the unique core concept: performing all processing on a small basis vector set and using matrix/vector multiplication afterwards for direct and fast transformation of the entire dataset. Experimentally, we demonstrate that a 703026 spectra image of chicken cartilage can be processed in 70 s (approximate to 0.1 ms / spectrum), which is approximate to 70 times faster than with the conventional workflow (approximate to 7.0 ms / spectrum). Additionally, we discuss how this method may be used for machine learning (ML) by re-using the transformed basis vector sets with new data. Using this ML paradigm, the same tissue image was processed (post-training) in approximate to 33 s, which is a speed-up of approximate to 150 times when compared with the conventional workflow.
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