Laser & Optoelectronics Progress, Volume. 57, Issue 22, 221009(2020)

Method of Haze Image Reconstruction Based on PolarizationLayering and Analysis of Airlight

Ziqi Shao1, Haihong Jin1,2, Lijin Qian1, and Zhiguo Fan1、*
Author Affiliations
  • 1School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui 230601, China
  • 2School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, Anhui 230601, China
  • show less

    To improve the accuracy of airlight estimation in polarization dehazing methods, a method for haze image reconstruction based on polarization layering and analysis of airlight is proposed. In the polarization space, the gradient prior information of the airlight is used as a constraint condition, and the original polarized hazy image is layered to estimate the polarized image of the airlight. This allows the analysis of the airlight from the polarized images, and the polarization layering and analysis of the airlight can be realized. Finally, by combining the proposed polarization reconstruction model of haze images and the estimation of atmospheric light at infinity in airlight images, a clear haze-free image is reconstructed. The experimental results show that the proposed method improves the accuracy of airlight estimation, provides a clearer reconstructed image, and provides a higher target restoration degree. The proposed method is suitable for haze image reconstruction under different concentrations.

    Tools

    Get Citation

    Copy Citation Text

    Ziqi Shao, Haihong Jin, Lijin Qian, Zhiguo Fan. Method of Haze Image Reconstruction Based on PolarizationLayering and Analysis of Airlight[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221009

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: Mar. 2, 2020

    Accepted: Apr. 10, 2020

    Published Online: Nov. 5, 2020

    The Author Email: Fan Zhiguo (fzghfut@163.com)

    DOI:10.3788/LOP57.221009

    Topics