Visibility restoration from single image based optical model

Qieshi Zhang, Sei Ichiro Kamata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a segmentation based method to estimate the haze-free image by the optical model. In this work, we estimate the atmospheric light by color barycenter hexagon (CBH) model and use the watershed to segment the image to calculate transmission map by dark pixels with single image. Firstly, non-color region is segmented by CBH model and calculate the atmospheric light. Then, use the watershed with rang component of CBH model to segment the color image into several sub-regions, and estimate the transmission map. Finally, use the optical model with the parameters to restore the haze-free image. The experimental results show that our method is more effective and able to get better results than other compared single image based methods.

Original languageEnglish
Title of host publicationVISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications
Pages213-216
Number of pages4
Publication statusPublished - 2012 Jun 15
EventInternational Conference on Computer Vision Theory and Applications, VISAPP 2012 - Rome, Italy
Duration: 2012 Feb 242012 Feb 26

Publication series

NameVISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications
Volume1

Conference

ConferenceInternational Conference on Computer Vision Theory and Applications, VISAPP 2012
CountryItaly
CityRome
Period12/2/2412/2/26

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Keywords

  • Color barycenter hexagon (CBH) model
  • Haze removal
  • Optical model
  • Single image
  • Watershed

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

Cite this

Zhang, Q., & Kamata, S. I. (2012). Visibility restoration from single image based optical model. In VISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications (pp. 213-216). (VISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications; Vol. 1).