Haze level evaluation using dark and bright channel prior information

Ying CHU, Fan CHEN, Hong FU, Hengyong YU

Research output: Contribution to journalArticlespeer-review

3 Citations (Scopus)


Haze level evaluation is highly desired in outdoor scene monitoring applications. However, there are relatively few approaches available in this area. In this paper, a novel haze level evaluation strategy for real-world outdoor scenes is presented. The idea is inspired by the utilization of dark and bright channel prior (DBCP) for haze removal. The change between hazy and haze-free scenes in bright channels could serve as a haze level indicator, and we have named it DBCP-I. The variation of contrast between dark and bright channels in a single hazy image also contains useful information to reflect haze level. By searching for a segmentation threshold, a metric called DBCP-II is proposed. Combining the strengths of the above two indicators, a hybrid metric named DBCP-III is constructed to achieve better performance. The experiment results on public, real-world benchmark datasets show the advantages of the proposed methods in terms of assessment accuracy with subjective human ratings. The study is first-of-its-kind with preliminary exploration in the field of haze level evaluation for real outdoor scenes, and it has a great potential to promote research in autonomous driving and automatic air quality monitoring. The open-source codes of the proposed algorithms are free to download. Copyright © 2022 by the authors.
Original languageEnglish
Article number683
Issue number5
Early online date24 Apr 2022
Publication statusPublished - May 2022


Chu, Y., Chen, F., Fu, H., & Yu, H. (2022). Haze level evaluation using dark and bright channel prior information. Atmosphere, 13(5). Retrieved from https://doi.org/10.3390/atmos13050683


  • Haze level evaluation
  • Dark channel prior
  • Bright channel prior
  • Image quality assessment
  • Air quality monitoring


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