An end-to-end fully-convolutional neural network for division of focal plane sensors to reconstruct S0, DoLP, and AoP

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