基于Color Lines先验的高阶马尔科夫随机场去雾

传统的一阶马尔科夫随机场在图像先验信息表达和对图像整体的约束上能力有限，同时基于暗通道的去雾算法在天空等大片白色区域处理效果存在偏差。针对以上问题，该文提出一种基于Color Lines 的高阶马尔科夫随机场去雾算法。该算法通过引入对颜色失真具有很好鲁棒性的Color Lines 先验条件，初步校正经暗通道获取的传输图，然后利用高阶马尔科夫随机场优化传输图，获取最终精确的去雾图像。实验结果表明，与已有算法相比，该文算法具有更强的普适性，可提高雾天图像的清晰度，同时恢复更多的图像细节等信息。

Higher-order Markov Random Fields Defogging Based on Color Lines

Compared with the first-order Markov random fields, higher-order Markov random fields could incorporate more statistical priors, thus have much expressive power of modeling. And the defogged images which based on dark channel prior have much error in sky regions and big white blocks. To solve those problems, this paper proposes a Markov random fields defogging method based on Color Lines. This method corrects the dark channel prior, according to the color lines which has a good robustness to color distortion, then uses the higher-order Markov random fields to optimize the transmission image to obtain final defogged image. The experimental results show that this method could improve the image resolution, while maintaining more image details.