Acta Optica Sinica, Volume. 39, Issue 6, 0611001(2019)

Ultraviolet-Visible Polarimetric Imaging and Image Fusion Technology with High Resolution and Large Field-of-View

Qingling Li1,2,3, Dayi Yin1,2,3、*, Jintao Yu1,2,3, and Lei Li1,2,3
Author Affiliations
  • 1 University of Chinese Academy of Sciences, Beijing 100049, China
  • 2 Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
  • 3 Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China
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    The Monte Carlo method is used to simulate the atmospheric polarimetric distribution under water clouds. The results show that the polarimetric response of water clouds in the ultraviolet (UV) band of 360-400 nm is the largest as compared to that of other spectra. Polarization pictures of buildings, clouds, and the sky in the same field of view are taken using UV-visible polarimetric imaging technology. Hough transform is used to divide these pictures, and statistical analysis is applied to each segment. The statistical results show that the relative differences of the polarization degree and polarization angle of the cloud-free and cloudy areas are -14% and 1.6%, respectively, providing the robustness of polarization angle in atmospheric detection. The UV and visible are found to be complementary in polarimetric detection for the clouds. Thus, image fusion technology in conjunction with a Laplacian pyramid can improve the detection capability for atmospheric targets. Results verify that the UV-visible polarimetric imaging technology with large field-of-view and high resolution is feasible and effective for atmospheric detection.

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    Qingling Li, Dayi Yin, Jintao Yu, Lei Li. Ultraviolet-Visible Polarimetric Imaging and Image Fusion Technology with High Resolution and Large Field-of-View[J]. Acta Optica Sinica, 2019, 39(6): 0611001

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    Paper Information

    Category: Imaging Systems

    Received: Dec. 29, 2018

    Accepted: Feb. 19, 2019

    Published Online: Jun. 17, 2019

    The Author Email: Yin Dayi (yindayi@mail.sitp.ac.cn)

    DOI:10.3788/AOS201939.0611001

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