Learning network for laser absorption imaging in flames using mid-fidelity simulations

Chuyu Wei; Kevin K. Schwarm; Daniel I. Pineda; R. Mitchell Spearrin

doi:10.1364/COSI.2021.CTh5A.6

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper CTh5A.6
•https://doi.org/10.1364/COSI.2021.CTh5A.6

Learning network for laser absorption imaging in flames using mid-fidelity simulations

Chuyu Wei, Kevin K. Schwarm, Daniel I. Pineda, and R. Mitchell Spearrin

Computational Optical Sensing and Imaging 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-89-7

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Inversion Methods for Computational Imaging (CTh5A)

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C. Wei, K. K. Schwarm, D. I. Pineda, and R. M. Spearrin, "Learning network for laser absorption imaging in flames using mid-fidelity simulations," in OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP), H. Hua, B. Javidi, M. Martinez-Corral, O. Matoba, A. Stern, S. Thibault, T. Alieva, J. Ke, F. Willomitzer, F. Okten, P. Silveira, P. Banerjee, E. Stoykova, D. Chu, J. Park, F. Imai, C. Joo, M. Digman, D. Gardner, S. Gladysz, D. LeMaster, S. Basu, and O. Korotkova, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper CTh5A.6.

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Abstract

A deep neural network is trained using mid-fidelity reacting flow simulations to assist laser absorption imaging of species and temperature in flames with sparse view angles. The method is compared to linear tomography.

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