Simultaneous demosaicing and resolution enhancement of LWIR DoFP polarimeter data

We recently presented a deep learning approach to demosaic division of focal plane (DoFP) imaging polarimeter data based upon a conditional generative adversarial network (cGAN). The approach was developed and demonstrated using visible DoFP polarimeter data and showed a notable ability to reduce false edge artifacts, aliasing, and temporal noise. Here we retrain and apply this algorithm to emissive-band polarimetric data acquired with a LWIR DoFP imaging polarimeter to investigate performance. We then adapt the baseline cGAN architecture to perform simultaneous demosaicing and resolution enhancement of LWIR DoFP data. We collect full-resolution polarized intensity data using a division-of-time (DoT) LWIR imaging polarimeter that we use to simulate decimated DoFP data for training and testing purposes. We then apply the algorithm to data obtained from simulated LWIR DoFP polarimeter data and assess performance.