Hazy images suffer from low visibility since the light gets scattered as it passes through various atmospheric particles. Moreover, such images are prone to color distortion, particularly in real weather conditions like sandstorms. In this letter, an effective dehazing technique is proposed using weighted least squares filtering on dark channel prior and color correction that involves automatic detection of color cast images. We show that the spread of the hue in a hazy image can differentiate a color cast image from a non-cast one. We propose a measure using the same for categorizing hazy images as cast and non-cast ones. Our novel color correction is performed by color balancing using a non-linear transformation followed by a cast-adaptive airlight refinement. Subjective and quantitative evaluations show that our method outperforms the state-of-the-art. It removes cast satisfactorily and reduces haze substantially while maintaining the naturalness of the image. Moreover, it produces visually pleasing images without halo artifacts.

中文翻译：

---

通过自适应空气光细化和非线性色彩平衡进行色偏相关图像去雾

朦胧的图像可见度低，因为光线在穿过各种大气粒子时会被散射。此外，此类图像容易出现颜色失真，尤其是在沙尘暴等真实天气条件下。在这封信中，提出了一种有效的去雾技术，在暗通道先验和颜色校正上使用加权最小二乘滤波，涉及自动检测偏色图像。我们表明，朦胧图像中色调的扩散可以将偏色图像与非偏色图像区分开来。我们提出了一种使用相同方法将朦胧图像分类为投射和非投射图像的度量。我们新颖的色彩校正是通过使用非线性变换进行色彩平衡然后进行自适应空气光细化来执行的。主观和定量评估表明，我们的方法优于最先进的方法。它可以令人满意地去除色偏并显着减少雾度，同时保持图像的自然度。此外，它可以产生视觉上令人愉悦的图像，而没有光晕伪影。