Zeng Xianglong, Luo Yuan, Zhao Xiaojing, Ye Wenbin
Opt Express. 2019 Mar 18;27(6):8566-8577. doi: 10.1364/OE.27.008566.
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, but 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), 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, 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.
焦平面分割（DoFP）偏振计广泛应用于偏振成像传感器中。集成在焦平面上的周期性排列的微偏振器确保了其出色的实时性能，但降低了输出图像的空间分辨率，并进一步影响偏振参数的计算。本文提出了一种名为Fork-Net的四层端到端全卷积神经网络，其目的是直接提高强度（即S）、线性偏振度（DoLP）和偏振角（AoP）这三种偏振特性的成像质量，而不是专注于减少不同偏振方向强度图像的插值误差。Fork-Net接受原始马赛克图像作为输入，并直接输出S、DoLP和AoP。它还使用定制的损失函数进行训练。实验结果表明，与现有方法相比，所提出的方法在输出图像上实现了最高的峰值信噪比（PSNR）和出色的视觉质量。
