期刊
OPTICS EXPRESS
卷 27, 期 6, 页码 8566-8577出版社
Optica Publishing Group
DOI: 10.1364/OE.27.008566
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资金
- National Natural Science Foundation of China [61601301]
- Fundamental Research Foundation of Shenzhen [JCYJ20170302151123 005]
Division of focal plane (DoFP) polarimeter is widely used in polarization imaging sensors. The periodically arranged micro-polarizers integrated on the focal plane ensure its outstanding real-time performance, hut reduce the spatial resolution of output images and further affect the calculation of polarization parameters. In this paper, a four layer, end-to-end fully convolutional neural network called Fork-Net is proposed, which aims to directly improve the imaging quality of three polarization properties: intensity (i.e., S-0), degree of linear polarization (DoLP), and angle of polarization (AoP), rather than focusing on reducing the interpolation error of intensity images of different polarization orientations. The Fork-Net accepts raw mosaic images as input and directly outputs S-0, DoLP, and AoP. It is also trained with a customized loss function. The experimental results show that compared with existing methods, the proposed one achieves the highest peak signal-to-noise ratio (PSNR) and prominent visual quality on output images. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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