Haze Removal via Regional Saturation-Value Translation and Soft
  Segmentation
Le-Anh Tran Dong-Chul Park
This paper proposes a single image dehazing prior, called Regional
Saturation-Value Translation (RSVT), to tackle the color distortion problems
caused by conventional dehazing approaches in bright regions. The RSVT prior is
developed based on two key observations regarding the relationship between hazy
and haze-free points in the HSV color space. First, the hue component shows
marginal variation between corresponding hazy and haze-free points,
consolidating a hypothesis that the pixel value variability induced by haze
primarily occurs in the saturation and value spaces. Second, in the 2D
saturation-value coordinate system, most lines passing through hazy-clean point
pairs are likely to intersect near the atmospheric light coordinates.
Accordingly, haze removal for the bright regions can be performed by properly
translating saturation-value coordinates. In addition, an effective soft
segmentation method based on a morphological min-max channel is introduced. By
combining the soft segmentation mask with the RSVT prior, a comprehensive
single image dehazing framework is devised. Experimental results on various
synthetic and realistic hazy image datasets demonstrate that the proposed
scheme successfully addresses color distortion issues and restores visually
appealing images. The code of this work is available at
https://github.com/tranleanh/rsvt.