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.

该研究提出了一种称为区域饱和度-值转换（RSVT）的单图像去雾先验，以解决常规去雾方法在亮区域中引起的色彩失真问题。通过将软分割掩模与RSVT先验相结合，在各种合成和真实模糊图像数据集上进行的实验证明该方案成功解决了色彩失真问题，并恢复了视觉吸引力的图像。

基于区域饱和度-值转换和软分割的雾霾去除