The nonresonant background (NRB) contribution to the coherent anti-Stokes Raman scattering (CARS) signal distorts the spectral line shapes and thus degrades the chemical information.

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Year:

2023

Interpolated inverse discrete wavelet transforms in additive and non-additive spectral background corrections

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Year:

2023

Effect of non-resonant background on the extraction of Raman signals from CARS spectra using deep neural networks

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Year:

2022

Semi-synthetic data generation to fine-tune a convolutional neural network for retrieving Raman signals from CARS spectra

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Year:

2022

Convolutional neural network-based retrieval of Raman signals from CARS spectra

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Year:

2022

Spatial localization of hotspots in Fano-resonant plasmonic oligomers for surface-enhanced coherent anti-Stokes Raman scattering

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Year:

2020

Bayesian Quantification for Coherent Anti-Stokes Raman Scattering Spectroscopy

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Year:

2020

Collaborators (1)

Lasse Lensu

Professor

Lappeenranta University of Technology

FINLAND

Erik M. Vartiainen

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Articles (9)

Log-Gaussian gamma processes for training Bayesian neural networks in Raman and CARS spectroscopies

Evaluating different deep learning models for efficient extraction of Raman signals from CARS spectra

Evaluating different deep learning models for efficient extraction of Raman signals from CARS spectra

Interpolated inverse discrete wavelet transforms in additive and non-additive spectral background corrections

Effect of non-resonant background on the extraction of Raman signals from CARS spectra using deep neural networks

Semi-synthetic data generation to fine-tune a convolutional neural network for retrieving Raman signals from CARS spectra

Convolutional neural network-based retrieval of Raman signals from CARS spectra

Spatial localization of hotspots in Fano-resonant plasmonic oligomers for surface-enhanced coherent anti-Stokes Raman scattering

Bayesian Quantification for Coherent Anti-Stokes Raman Scattering Spectroscopy

Collaborators (1)

Lasse Lensu

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