Single Image Dehazing Using Haze-Lines

Dana Berman, Tali Treibitz, Shai Avidan

Research output: Contribution to journalArticlepeer-review


Haze often limits visibility and reduces contrast in outdoor images. The degradation varies spatially since it depends on the objects' distances from the camera. This dependency is expressed in the transmission coefficients, which control the attenuation. Restoring the scene radiance from a single image is a highly ill-posed problem, and thus requires using an image prior. Contrary to methods that use patch-based image priors, we propose an algorithm based on a non-local prior. The algorithm relies on the assumption that colors of a haze-free image are well approximated by a few hundred distinct colors, which form tight clusters in RGB space. Our key observation is that pixels in a given cluster are often non-local, i.e., spread over the entire image plane and located at different distances from the camera. In the presence of haze these varying distances translate to different transmission coefficients. Therefore, each color cluster in the clear image becomes a line in RGB space, that we term a haze-line. Using these haze-lines, our algorithm recovers the atmospheric light, the distance map and the haze-free image. The algorithm has linear complexity, requires no training, and performs well on a wide variety of images compared to other state-of-the-art methods.

Original languageEnglish
Article number8540862
Pages (from-to)720-734
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number3
StatePublished - 1 Mar 2020

Bibliographical note

Publisher Copyright:
© 1979-2012 IEEE.


  • Single image dehazing
  • haze removal

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics


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