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Proceedings Paper • new

Deep-learning optics
Author(s): Aydogan Ozcan
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Paper Abstract

In the first part of this presentation, we will discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable image enhancement and new transformations among different modalities of microscopic imaging, driven entirely by image data. In this second part, we introduce a physical mechanism to perform machine learning by demonstrating a Diffractive Deep Neural Network architecture that can all-optically implement various functions following the deep learning-based design of passive layers that work collectively. We created 3D-printed diffractive networks that implement classification of images of handwritten digits and fashion products as well as the function of an imaging lens at terahertz spectrum. This passive diffractive network can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that perform unique tasks using diffractive neural networks.

Paper Details

Date Published: 8 March 2019
PDF
Proc. SPIE 10937, Optical Data Science II, 1093702 (8 March 2019); doi: 10.1117/12.2518571
Show Author Affiliations
Aydogan Ozcan, California NanoSystems Institute (United States)
Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 10937:
Optical Data Science II
Bahram Jalali; Ken-ichi Kitayama, Editor(s)

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