旋光法
光学
鬼影成像
人工神经网络
计算机科学
极化(电化学)
人工智能
物理
光强度
计算机视觉
散射
物理化学
化学
作者
Haofeng Hu,Lin Yang,Xiaobo Li,Pengfei Qi,Tiegen Liu
出处
期刊:Optics Letters
[The Optical Society]
日期:2020-10-16
卷期号:45 (22): 6162-6162
被引量:47
摘要
Imaging in low light is significant but challenging in many applications. Adding the polarization information into the imaging system compromises the drawbacks of the conventional intensity imaging to some extent. However, generally speaking, the qualities of intensity images and polarization images cannot be compatible due to the characteristic differences in polarimetric operators. In this Letter, we collected, to the best of our knowledge, the first polarimetric imaging dataset in low light and present a specially designed neural network to enhance the image qualities of intensity and polarization simultaneously. Both indoor and outdoor experiments demonstrate the effectiveness and superiority of this neural network-based solution, which may find important applications for object detection and vision in photon-starved environments.
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