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Proceedings Paper

Learning from examples in optical tomography (Conference Presentation)
Author(s): Demetri Psaltis
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Paper Abstract

An optical tomography system measures the light scattered by an object as a function of spatial coordinates and as a function of the illumination angle. The measured signals are digitally processed to produce a 3D image of the object. In this paper we describe how we can learn the shape of an object by constructing a neural network that models the optical system and training the network to match the experimentally measured data. The variables of the trained network yield the image of the unknown object at the end of training phase. [1] Ulugbek, Papadopoulos, Shoreh, Goy, Vonesh, Unser, Psaltis, “A Learning Approach to Optical Tomography” Optica, May 2015.

Paper Details

Date Published: 27 April 2016
PDF: 1 pages
Proc. SPIE 9718, Quantitative Phase Imaging II, 97181Z (27 April 2016); doi: 10.1117/12.2212785
Show Author Affiliations
Demetri Psaltis, Ecole Polytechnique Fédérale de Lausanne (Switzerland)

Published in SPIE Proceedings Vol. 9718:
Quantitative Phase Imaging II
Gabriel Popescu; YongKeun Park, Editor(s)

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