Heavy haze removal in a learning framework

Jie Chen, Lap Pui Chau

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


Extreme weather hazards happens more often these days due to climate changes and increased human industrial activities, and one of most notorious of them is haze. State-of-the-art haze removal methods generally work well with light haze conditions, however when haze gets heavier, the physical model tend to produce over-shadowed, noisy, and color distorted restorations. A new physical model has been proposed in this paper for heavy haze weathers. An airlight vector map has been proposed to address the problem caused by uneven aerosol distribution w.r.t. altitude variation. A Random Decision Forest model has been adopted to deal with the additional light attenuation and transmission map underestimation problem caused by heavy haze. Experiment shows the proposed model produces much better visual restoration for heavy haze weathers compared to state-of-the-art methods in terms of colour fidelity, noise reduction, and overall contrast.

Original languageEnglish
Title of host publication2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781479983919
Publication statusPublished - 27 Jul 2015
Externally publishedYes
EventIEEE International Symposium on Circuits and Systems, ISCAS 2015 - Lisbon, Portugal
Duration: 24 May 201527 May 2015

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310


ConferenceIEEE International Symposium on Circuits and Systems, ISCAS 2015


  • dark channel prior
  • extremely heavy haze
  • random decision forest

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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