A variational framework for single image Dehazing

Adrian Galdran*, Javier Vazquez-Corral, David Pardo, Marcelo Bertalmío

*Corresponding author for this work

Research output: Chapter in BookChapterResearchpeer-review

25 Citations (Scopus)


Images captured under adverse weather conditions, such as haze or fog, typically exhibit low contrast and faded colors, which may severely limit the visibility within the scene. Unveiling the image structure under the haze layer and recovering vivid colors out of a single image remains a challenging task, since the degradation is depth-dependent and conventional methods are unable to handle this problem. We propose to extend a well-known perception-inspired variational framework [1] for the task of single image dehazing. The main modification consists on the replacement of the value used by this framework for the grey-world hypothesis by an estimation of the mean of the clean image. This allows us to devise a variational method that requires no estimate of the depth structure of the scene, performing a spatially-variant contrast enhancement that effectively removes haze from far away regions. Experimental results show that our method competes well with other state-of-the-art methods in typical benchmark images, while outperforming current image dehazing methods in more challenging scenarios.

Original languageEnglish
Title of host publicationComputer Vision - ECCV 2014 Workshops, Proceedings
EditorsCarsten Rother, Lourdes Agapito, Michael M. Bronstein
Number of pages12
ISBN (Electronic)9783319161983
Publication statusPublished - 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Color correction
  • Contrast enhancement
  • Image defogging
  • Image dehazing


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