From Event: SPIE Optical Engineering + Applications, 2017
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.
Garrett C. Sargent, Bradley M. Ratliff, and Vijayan K. Asari, "Single image super-resolution via regularized extreme learning regression for imagery from microgrid polarimeters," Proc. SPIE 10407, Polarization Science and Remote Sensing VIII, 104070C (Presented at SPIE Optical Engineering + Applications: August 08, 2017; Published: 30 August 2017); https://doi.org/10.1117/12.2273945.
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