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2D and 3D computational optical imaging using deep convolutional neural networks (DCNNs)
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Paper Abstract

We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D imaging through scattering media. The inverse scattering problem is solved based on learning a huge number of training target and speckle pairs. The proposed technique does not rely on a reference beam, thus employs a simpler optical setup than previous techniques without the need to know the imaging model and optical processes. This lack of the need to know a prior model of the forward operator is very important since many optimization techniques are very sensitive to errors caused by the inaccuracy of the forward model.

Paper Details

Date Published: 14 May 2018
PDF: 8 pages
Proc. SPIE 10667, Dimensional Optical Metrology and Inspection for Practical Applications VII, 1066702 (14 May 2018); doi: 10.1117/12.2303995
Show Author Affiliations
Thanh Nguyen, The Catholic Univ. of America (United States)
George Nehmetallah, The Catholic Univ. of America (United States)


Published in SPIE Proceedings Vol. 10667:
Dimensional Optical Metrology and Inspection for Practical Applications VII
Kevin G. Harding; Song Zhang, Editor(s)

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