Abstract
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.
© 2020 Optical Society of America
Full Article | PDF ArticleMore Like This
Hedong Liu, Yizhu Zhang, Zhenzhou Cheng, Jingsheng Zhai, and Haofeng Hu
Opt. Lett. 47(11) 2726-2729 (2022)
Junchao Zhang, Jianbo Shao, Jianlai Chen, Degui Yang, Buge Liang, and Rongguang Liang
Opt. Lett. 45(6) 1507-1510 (2020)
Yuanyuan Sun, Junchao Zhang, and Rongguang Liang
Opt. Lett. 46(17) 4338-4341 (2021)