PENTAGON: Physics-enhanced neural network for volumetric flame chemiluminescence tomography
This study proposes a physics-enhanced neural network, PENTAGON, as an inference framework for volumetric tomography applications. By leveraging the synergistic combination of data-prior and forward-imaging model, we can accurately predict 3D optical fields, even when the number of projection views...
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Published in | Optics express Vol. 32; no. 19; p. 32732 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
09.09.2024
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Online Access | Get full text |
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Summary: | This study proposes a physics-enhanced neural network, PENTAGON, as an inference framework for volumetric tomography applications. By leveraging the synergistic combination of data-prior and forward-imaging model, we can accurately predict 3D optical fields, even when the number of projection views decreases to three. PENTAGON is proven to overcome the generalization limitation of data-driven deep learning methods due to data distribution shift, and eliminate distortions introduced by conventional iteration algorithms with limited projections. We evaluated PENTAGON using numerical and experimental results of a flame chemiluminescence tomography example. Results showed that PENTAGON can potentially be generalized for inverse tomography reconstruction problems in many fields. |
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ISSN: | 1094-4087 1094-4087 |
DOI: | 10.1364/OE.536550 |