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A guided generative adversarial network demosaicing strategy for integrated microgrid polarimeter imagery

Division of focal plane (DoFP), or integrated microgrid imaging polarimeters, typically consist of a 2x2 mosaic of linear polarization filters overlaid upon a focal plane array sensor and obtain temporally synchronized polarized intensity measurements across a scene, similar in concept to a Bayer color filter array camera. However, the resulting estimated polarimetric images suffer a loss in resolution and can be plagued by aliasing due to the modulated microgrid measurement strategy. Demosaicing strategies have been proposed that attempt to minimize these effects, but result in some level of residual artifacts. In this work, we present a conditional and guided generative adversarial network (GAN) strategy for demosaicing integrated microgrid polarimeter imagery. The GAN is trained using high resolution polarized intensity measurements that contain minimal spatial aliasing artifacts obtained from a division-of-time polarimeter. We apply the algorithm to test data collected from real visible microgrid imagery and compare the results with other state-of-the-art microgrid demosaicing strategies.