Conditional generative adversarial network demosaicing strategy for division of focal plane polarimeters

Division of focal plane (DoFP), or integrated microgrid polarimeters, typically consist of a 2 × 2 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 spatially-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 propose a conditional generative adversarial network (cGAN) approach to the microgrid demosaicing problem. We evaluate the performance of our approach against full-resolution division-of-time polarimeter data as well as compare against both traditional and recent microgrid demosaicing methods. We apply these demosaicing strategies to data from both real and simulated visible microgrid imagery and provide an objective criteria for evaluating their performance. We demonstrate that the proposed cGAN approach results in estimated Stokes imagery that is comparable to full-resolution ground truth imagery from both a quantitative and qualitative perspective.