We propose a polarization demosaicing convolutional neural network to address the image
demosaicing issue, the last unsolved issue in microgrid polarimeters. This network learns an
end-to-end mapping between the mosaic images and full-resolution ones. Skip connections
and customized loss function are used to boost the performance. Experimental results show
that our proposed network outperforms other state-of-the-art methods by a large margin in
terms of quantitative measures and visual quality.