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4 October 2022 Using untrained, physics-informed neural networks for structured illumination image reconstruction (Conference Presentation)
Zachary Burns, Zhaowei Liu
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Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC1220416 (2022) https://doi.org/10.1117/12.2633621
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States
Abstract
In recent years, new forms of structured illumination microscopy (SIM) have used near-field illumination from metamaterial substrates to increase resolution improvements past 2x. We demonstrate that the forward model of SIM can be used as the loss function to optimize a neural network on a single set of diffraction-limited sub-images. We show that this physics-informed neural network (PINN) can be used with a variety of structured illumination methods such as plasmonic and metamaterial SIM to achieve resolution improvements of 3x and 4x.
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Zachary Burns and Zhaowei Liu "Using untrained, physics-informed neural networks for structured illumination image reconstruction (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC1220416 (4 October 2022); https://doi.org/10.1117/12.2633621
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KEYWORDS
Neural networks
Image restoration
Illumination engineering
Near field
Metamaterials
Microscopy
Plasmonics
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