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Color polarization demosaicking by a convolutional neural network

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Abstract

To address color polarization demosaicking problems in polarization imaging with a color polarization camera, we propose a color polarization demosaicking convolutional neural network (CPDCNN), which has a two-branch structure to ensure the fidelity of polarization signatures and enhance image resolution. To train the network, we built a unique dual-camera system and captured a pairwise color polarization image dataset. Experimental results show that CPDCNN outperformances other methods by a large margin in contrast and resolution.

© 2021 Optical Society of America

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Data Availability

Data underlying the results presented in this Letter are available from [17].

17. https://wp.optics.arizona.edu/ualiangaol/publications/experimental-data/.

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