Abstract

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.

© 2018 Optical Society of America

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