Image acquisition under bad weather conditions is prone to yield image with low contrast, faded color, and overall poor visibility. Different computer vision applications including surveillance, object classification, tracking, and recognition get effected due to degraded hazy images. Dehazing can significantly improve contrast, balance luminance, correct distortion, remove unwanted visual effects/ and therefore enhance the image quality. As a result, image defogging is imperative pre-processing step in computer vision applications. Previously, dark channel prior-based algorithms have proven promising results over the available techniques. In this paper, we have proposed a modified dark channel prior that uses fog density and guided image-filtering technique to estimate and refine transmission map, respectively. Guided image filter speeds up the refinement of transmission map, hence reduces the overall computational complexity of algorithm. We have also incorporated segmentation of the foggy image into sky and non-sky regions, after which, the modified dark channel prior and atmospheric light is computed for each segment. Then, the average value of atmospheric light for each segment is used to estimate transmission map. We have performed quantitative and subjective comparison for effective evaluation of our proposed algorithm against the current state-of-the-art algorithms on natural and synthetic images. Different quality metrics, such as saturation, mean square error, fog density, peak signal to noise ratio, structural similarity index metric, dehazing algorithm index (DHQI), full-reference image quality assessmen (FR-IQA), and naturalness of dehazed images have shown the proposed algorithm to be better than existing techniques.

中文翻译：

---

使用改进的暗通道先验的基于分割的图像除雾

恶劣天气条件下的图像采集容易产生对比度低，颜色褪色和整体可见度差的图像。由于模糊图像的退化，实现了包括监视，对象分类，跟踪和识别在内的各种计算机视觉应用。除雾可以显着改善对比度，平衡亮度，纠正失真，消除不必要的视觉效果/并因此提高图像质量。结果，在计算机视觉应用中，图像除雾是必不可少的预处理步骤。以前，基于暗通道先验的算法已证明优于现有技术。在本文中，我们提出了一种改进的暗通道先验，该暗通道先使用雾密度和引导图像滤波技术分别估计和细化了透射图。导引图像滤波器加速了传输图的细化，因此降低了算法的整体计算复杂度。我们还将雾图像的分割合并到天空和非天空区域中，然后，针对每个分割计算出修改后的暗通道先验光和大气光。然后，将每个部分的大气光的平均值用于估计透射图。我们已经进行了定量和主观的比较，以便针对自然和合成图像上的当前最新算法对我们提出的算法进行有效评估。不同的质量指标，例如饱和度，均方误差，雾密度，峰值信噪比，结构相似性指标指标，除雾算法指标（DHQI），全参考图像质量评估师（FR-IQA），