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Robust Image and Video Dehazing with Visual Artifact Suppression via Gradient Residual Minimization

  • Chen ChenEmail author
  • Minh N. Do
  • Jue Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9906)

Abstract

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.

Keywords

Video dehazing Image dehazing Contrast enhancement Artifact suppression 

Supplementary material

419974_1_En_36_MOESM1_ESM.zip (62.4 mb)
Supplementary material 1 (zip 63850 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.Adobe ResearchSeattleUSA

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