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We present a new collection of processing techniques, collectively"factorized Kramers--Kronig and error correction" (fKK-EC), for (a) Ramansignal extraction, (b) denoising, and (c) phase- and scale-error correction incoherent anti-Stokes Raman scattering (CARS) hyperspectral imaging andspectroscopy. These new methods are orders-of-magnitude faster thanconventional methods and are capable of real-time performance, owing to theunique core concept: performing all processing on a small basis vector set andusing matrix/vector multiplication afterwards for direct and fasttransformation of the entire dataset. Experimentally, we demonstrate that a703026 spectra image of chicken cartilage can be processed in 70 s(approximately 0.1 ms / spectrum), which is approximately 70 times faster thanwith the conventional workflow (approximately 7.0 ms / spectrum). Additionally,we discuss how this method may be used for machine learning (ML) by re-usingthe transformed basis vector sets with new data. Using this ML paradigm, thesame tissue image was processed (post-training) in approximately 33 s, which isa speed-up of approximately 150 times when compared with the conventionalworkflow.

Real-time and high-throughput Raman signal extraction and processing in CARS hyperspectral imaging