IEEE Trans Image Process. 2023;32:3238-3253. doi: 10.1109/TIP.2023.3279980. Epub 2023 Jun 7.
Saturation information in hazy images is conducive to effective haze removal, However, existing saturation-based dehazing methods just focus on the saturation value of each pixel itself, while the higher-level distribution characteristic between pixels regarding saturation remains to be harnessed. In this paper, we observe that the pixels, which share the same surface reflectance coefficient in the local patches of haze-free images, exhibit a linear relationship between their saturation component and the reciprocal of their brightness component in the corresponding hazy images normalized by atmospheric light. Furthermore, the intercept of the line described by this linear relationship on the saturation axis is exactly the saturation value of these pixels in the haze-free images. Using this characteristic of saturation, termed saturation line prior (SLP), the transmission estimation is translated into the construction of saturation lines. Accordingly, a new dehazing framework using SLP is proposed, which employs the intrinsic relevance between pixels to achieve a reliable saturation line construction for transmission estimation. This approach can recover the fine details and attain realistic colors from hazy scenes, resulting in a remarkable visibility improvement. Extensive experiments in real-world and synthetic hazy images show that the proposed method performs favorably against state-of-the-art dehazing methods. Code is available on https://github.com/LPengYang/Saturation-Line-Prior.
雾天图像中的饱和度信息有助于有效地去除雾度，然而，现有的基于饱和度的去雾方法仅仅关注每个像素自身的饱和度值，而像素之间关于饱和度的更高层次的分布特征仍有待利用。在本文中，我们观察到，在无雾图像的局部斑块中具有相同表面反射系数的像素，在相应的雾天图像中，其饱和度分量与亮度分量的倒数之间存在线性关系，该亮度分量由大气光归一化。此外，该线性关系描述的直线在饱和度轴上的截距恰好是这些像素在无雾图像中的饱和度值。利用这种称为饱和度线先验（SLP）的饱和度特性，传输估计被转化为饱和度线的构建。因此，提出了一种新的基于 SLP 的去雾框架，它利用像素之间的内在相关性来实现可靠的传输估计的饱和度线构建。该方法可以从雾天场景中恢复精细的细节并获得逼真的颜色，从而显著提高能见度。在真实和合成雾天图像中的广泛实验表明，所提出的方法优于最先进的去雾方法。代码可在 https://github.com/LPengYang/Saturation-Line-Prior 上获得。
