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

This paper introduces a new matting task called human instance matting (HIM),
which requires the pertinent model to automatically predict a precise alpha
matte for each human instance. Straightforward combination of closely related
techniques, namely, instance segmentation, soft segmentation and
human/conventional matting, will easily fail in complex cases requiring
disentangling mingled colors belonging to multiple instances along hairy and
thin boundary structures. To tackle these technical challenges, we propose a
human instance matting framework, called InstMatt, where a novel mutual
guidance strategy working in tandem with a multi-instance refinement module is
used, for delineating multi-instance relationship among humans with complex and
overlapping boundaries if present. A new instance matting metric called
instance matting quality (IMQ) is proposed, which addresses the absence of a
unified and fair means of evaluation emphasizing both instance recognition and
matting quality. Finally, we construct a HIM benchmark for evaluation, which
comprises of both synthetic and natural benchmark images. In addition to
thorough experimental results on complex cases with multiple and overlapping
human instances each has intricate boundaries, preliminary results are
presented on general instance matting. Code and benchmark are available in
this https URL

提出了一种称为人物实例抠图（HIM）的新型抠图任务，旨在自动预测每个人物实例的精确 alpha 抠图。通过引入名为 InstMatt 的人物实例抠图框架，并使用新颖的互相指导策略以及多实例细化模块应对复杂的情况，本文提高了抠图的质量和准确性。我们还提出了一种新的实例抠图质量度量标准（IMQ），并构建了 HIM 基准测试集，包括合成和自然图像。