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
this https URL

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

Haze Removal via Regional Saturation-Value Translation and Soft  Segmentation

Grounding is crucial for natural language understanding. An important subtask
is to understand modified color expressions, such as 'dirty blue'. We present a
model of color modifiers that, compared with previous additive models in RGB
space, learns more complex transformations. In addition, we present a model
that operates in the HSV color space. We show that certain adjectives are
better modeled in that space. To account for all modifiers, we train a hard
ensemble model that selects a color space depending on the modifier color pair.
Experimental results show significant and consistent improvements compared to
the state-of-the-art baseline model.

本研究展示了一个对于自然语言中颜色修饰的理解模型，该模型比之前在 RGB 颜色空间中的加法模型能够学习更复杂的变换，同时我们还提出了一个在 HSV 颜色空间中操作的模型，结果表明针对某些形容词在 HSV 颜色空间中效果更好，此外我们还训练了一种硬强集成模型，该模型会根据修饰符颜色配对情况来选择颜色空间，实验结果表明与现有基线模型相比，该模型有显著且一致的提升。