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- W4283380442 abstract "Removing distortions in coherent anti-Stokes Raman scattering (CARS) spectra due to interference with the nonresonant background (NRB) is vital for quantitative analysis. Popular computational approaches, the Kramers-Kronig relation and the maximum entropy method, have demonstrated success but may generate significant errors due to peaks that extend in any part beyond the recording window. In this work, we present a learned matrix approach to the discrete Hilbert transform that is easy to implement, fast, and dramatically improves accuracy of Raman retrieval using the Kramers-Kronig approach." @default.
- W4283380442 created "2022-06-25" @default.
- W4283380442 creator A5043895567 @default.
- W4283380442 date "2022-07-05" @default.
- W4283380442 modified "2023-09-30" @default.
- W4283380442 title "Raman signal extraction from CARS spectra using a learned-matrix representation of the discrete Hilbert transform" @default.
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- W4283380442 doi "https://doi.org/10.1364/oe.460543" @default.
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