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Proceedings Paper

Image disambiguation with deep neural networks
Author(s): Omar DeGuchy; Alex Ho; Roummel F. Marcia
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Paper Abstract

In many signal recovery applications, measurement data is comprised of multiple signals observed concurrently. For instance, in multiplexed imaging, several scene subimages are sensed simultaneously using a single detector. This technique allows for a wider field-of-view without requiring a larger focal plane array. However, the resulting measurement is a superposition of multiple images that must be separated into distinct components. In this paper, we explore deep neural network architectures for this image disambiguation process. In particular, we investigate how existing training data can be leveraged and improve performance. We demonstrate the effectiveness of our proposed methods on numerical experiments using the MNIST dataset.

Paper Details

Date Published: 6 September 2019
PDF: 7 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390A (6 September 2019);
Show Author Affiliations
Omar DeGuchy, Univ. of California, Merced (United States)
Alex Ho, Univ. of California, Merced (United States)
Roummel F. Marcia, Univ. of California, Merced (United States)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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