Learning a convolutional demosaicing network for microgrid polarimeter imagery

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.