Presentation
13 March 2024 Diffractive visual processors
Author Affiliations +
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. In this talk, I will give examples of each group, enabling transformative capabilities for various applications of interest in e.g., autonomous systems, defense/security, telecommunications as well as biomedical imaging and sensing.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Aydogan Ozcan "Diffractive visual processors", Proc. SPIE PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, PC128570Q (13 March 2024); https://doi.org/10.1117/12.3011386
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KEYWORDS
Optical design

Visualization

Cameras

Deep learning

Image processing

Imaging systems

Sensing systems

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