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Single image super-resolution via regularized extreme learning regression for imagery from microgrid polarimeters
Author(s): Garrett C. Sargent; Bradley M. Ratliff; Vijayan K. Asari
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Paper Abstract

The advantage of division of focal plane imaging polarimeters is their ability to obtain temporally synchronized intensity measurements across a scene; however, they sacrifice spatial resolution in doing so due to their spatially modulated arrangement of the pixel-to-pixel polarizers and often result in aliased imagery. Here, we propose a super-resolution method based upon two previously trained extreme learning machines (ELM) that attempt to recover missing high frequency and low frequency content beyond the spatial resolution of the sensor. This method yields a computationally fast and simple way of recovering lost high and low frequency content from demosaicing raw microgrid polarimetric imagery. The proposed method outperforms other state-of-the-art single-image super-resolution algorithms in terms of structural similarity and peak signal-to-noise ratio.

Paper Details

Date Published: 30 August 2017
PDF: 12 pages
Proc. SPIE 10407, Polarization Science and Remote Sensing VIII, 104070C (30 August 2017); doi: 10.1117/12.2273945
Show Author Affiliations
Garrett C. Sargent, Univ. of Dayton (United States)
Bradley M. Ratliff, Univ. of Dayton Research Institute (United States)
Vijayan K. Asari, Univ. of Dayton (United States)


Published in SPIE Proceedings Vol. 10407:
Polarization Science and Remote Sensing VIII
Joseph A. Shaw; Frans Snik, Editor(s)

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