Enhancement of image degraded by fog using cost function based on human visual model

Dongjun Kim, Changwon Jeon, Bonghyup Kang, Hanseok Ko

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

7 Citations (Scopus)

Abstract

In foggy weather conditions, images become degraded due to the presence of airlight that is generated by scattering light by fog particles. In this paper, we propose an effective method to correct the degraded image by subtracting the estimated airlight map from the degraded image. The airlight map is generated using multiple linear regression, which models the relationship between regional airlight and the coordinates of the image pixels. Airlight can then be estimated using a cost function that is based on the human visual model, wherein a human is more insensitive to variations of the luminance in bright regions than in dark regions. For this objective, the luminance image is employed for airlight estimation. The luminance image is generated by an appropriate fusion of the R, G, and B components. Representative experiments on real foggy images confirm significant enhancement in image quality over the degraded image.

Original languageEnglish
Title of host publicationLecture Notes in Electrical Engineering
Pages163-171
Number of pages9
Volume35 LNEE
DOIs
Publication statusPublished - 2009 Sep 25
Event7th IEEE International Conference on Multi-Sensor Integration and Fusion, IEEE MFI 2008 - Seoul, Korea, Republic of
Duration: 2008 Aug 202008 Aug 22

Publication series

NameLecture Notes in Electrical Engineering
Volume35 LNEE
ISSN (Print)18761100
ISSN (Electronic)18761119

Other

Other7th IEEE International Conference on Multi-Sensor Integration and Fusion, IEEE MFI 2008
CountryKorea, Republic of
CitySeoul
Period08/8/2008/8/22

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ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Kim, D., Jeon, C., Kang, B., & Ko, H. (2009). Enhancement of image degraded by fog using cost function based on human visual model. In Lecture Notes in Electrical Engineering (Vol. 35 LNEE, pp. 163-171). (Lecture Notes in Electrical Engineering; Vol. 35 LNEE). https://doi.org/10.1007/978-3-540-89859-7_12