Robust image and video dehazing with visual artifact suppression via gradient residual minimization

Chen Chen, Minh N. Do, Jue Wang

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


Most existing image dehazing methods tend to boost local image contrast for regions with heavy haze. Without special treatment, these methods may significantly amplify existing image artifacts such as noise, color aliasing and blocking, which are mostly invisible in the input images but are visually intruding in the results. This is especially the case for low quality cellphone shots or compressed video frames. The recent work of Li et al. (2014) addresses blocking artifacts for dehazing, but is insufficient to handle other artifacts. In this paper, we propose a new method for reliable suppression of different types of visual artifacts in image and video dehazing. Our method makes contributions in both the haze estimation step and the image recovery step. Firstly, an image-guided, depth-edge-aware smoothing algorithm is proposed to refine the initial atmosphere transmission map generated by local priors. In the image recovery process, we propose Gradient Residual Minimization (GRM) for jointly recovering the haze-free image while explicitly minimizing possible visual artifacts in it. Our evaluation suggests that the proposed method can generate results with much less visual artifacts than previous approaches for lower quality inputs such as compressed video clips.

Original languageEnglish (US)
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsBastian Leibe, Nicu Sebe, Max Welling, Jiri Matas
Number of pages16
ISBN (Print)9783319464749
StatePublished - Jan 1 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: Oct 11 2016Oct 14 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9906 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th European Conference on Computer Vision, ECCV 2016


  • Artifact suppression
  • Contrast enhancement
  • Image dehazing
  • Video dehazing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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