Efficient single image dehazing via scene-adaptive segmentation and improved dark channel model

He Zhang, Xin Liu*, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this paper, we present an efficient single image dehazing approach via scene-adaptive segmentation and improved dark channel model. First, we detect the image depth information and segment the raw image into the close view and distant view. Then, we utilize the minimum channel image of distant view to regularize the atmospheric veil and simultaneously estimate its light value of close view within the haze-opaque area, through which the whole transmission map can be well optimized. Finally, the haze degraded image can be well restored via the atmosphere scattering model. The experimental results have shown that the proposed single image dehazing approach has significantly increased the perceptual visibility of the scene and achieved a better color fidelity visually.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3440-3445
Number of pages6
ISBN (Electronic)9781509006199
DOIs
Publication statusPublished - 31 Oct 2016
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2016-October

Conference

Conference2016 International Joint Conference on Neural Networks, IJCNN 2016
Country/TerritoryCanada
CityVancouver
Period24/07/1629/07/16

Scopus Subject Areas

  • Software
  • Artificial Intelligence

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