Classification of Turbulence-Degraded Imagery Using Neural Networks

Daniel A. LeMaster; Steven Leung; Olga L. Mendoza-Schrock

doi:10.1364/PCAOP.2021.PTu4C.1

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper PTu4C.1
•https://doi.org/10.1364/PCAOP.2021.PTu4C.1

Classification of Turbulence-Degraded Imagery Using Neural Networks

Daniel A. LeMaster, Steven Leung, and Olga L. Mendoza-Schrock

Propagation Through and Characterization of Atmospheric and Oceanic Phenomena 2021

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

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Imaging through Turbulence (PTu4C)

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D. A. LeMaster, S. Leung, and O. L. Mendoza-Schrock, "Classification of Turbulence-Degraded Imagery Using Neural Networks," 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 PTu4C.1.

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Abstract

A more realistic simulation is used to evaluate claims made in a recently published work on object classification for turbulence degraded imagery.

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