DoLP and AoP Synthesis from division of focal plane polarimeters using CycleGAN

Abstract

Division of focal plane (DoFP) polarimeter brings significant advantages for capturing polarization information, and its unique structure enables temporal synchronization of polarization intensity measurements across scenes. While DoFP can obtain polarization information in all four directions simultaneously, it also reduces the spatial resolution of the image to 1/4 of the camera resolution. In order to solve low resolution and the cumulative error caused by the formula calculation. Using CycleGAN to construct a raw mosaic image-to-polarization properties images model, which differs from general interpolation methods and other existing deep learning methods with paired datasets and takes microgrid images as input and outputs the degree of linear polarization (DoLP) and angle of polarization (AoP) directly by learning the mapping function between unpaired the mosaic images and the polarization properties. In addition, Gradient Magnitude Similarity Deviation (GMSD) metric is applied to preserve satisfactory reconstructed image edge contour information and reduce IFOV artifacts and aliasing. The experimental results demonstrate that the quantitative performance evaluation metrics scores of our method are all higher than other methods and the reconstructed DoLP and AoP images are comparable to the ground truth imagery in terms of visual quality. The end-to-end network structure trained on unpaired datasets solves the severe limitations of paired datasets training, is more suitable for DoLP and AoP reconstructing.