Deep-learning-enabled computational microscopy and diffractive imaging

Aydogan Ozcan

doi:10.1117/12.2683772

4 October 2023 Deep-learning-enabled computational microscopy and diffractive imaging

Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC12746, SPIE-CLP Conference on Advanced Photonics 2023; PC127460G (2023) https://doi.org/10.1117/12.2683772
Event: SPIE-CLP Conference on Advanced Photonics, 2023, San Diego, California, United States

Abstract

I will discuss diffractive optical networks designed by deep learning to all-optically implement various complex functions as the input light diffracts through spatially-engineered surfaces. These diffractive processors designed by deep learning have various applications, e.g., all-optical image analysis, feature detection, object classification, computational imaging and seeing through diffusers, also enabling task-specific camera designs and new optical components for spatial, spectral and temporal beam shaping and spatially-controlled wavelength division multiplexing. These deep learning-designed diffractive systems can broadly impact (1) all-optical statistical inference engines, (2) computational camera and microscope designs and (3) inverse design of optical systems that are task-specific. I will give examples of each group, enabling transformative capabilities for various applications, e.g., autonomous systems, defense/security, telecommunications and biomedical imaging/sensing.

Conference Presentation

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Aydogan Ozcan "Deep-learning-enabled computational microscopy and diffractive imaging", Proc. SPIE PC12746, SPIE-CLP Conference on Advanced Photonics 2023, PC127460G (4 October 2023); https://doi.org/10.1117/12.2683772

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KEYWORDS

Microscopy

Optical design

Cameras

Deep learning

Image processing

Optical networks

Optical surfaces

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