Abstract: Since the dark channel prior algorithm can recover images with different degrees of fog and scene depth, and NL-CTV（Non-Local Color Total Variation） model based on the non-local operators can maintain the image features better, a single color image dehazing algorithm on the basis of dark channel prior and NL-CTV model is proposed. Firstly, obtaining the atmospheric light and the transmission from the dark channel prior accurately. Then, deriving the non-local energy function which contains the atmospheric light and transmission. Finally, using auxiliary variables and Bregman iterative parameters to calculate the energy function, and designing its split algorithm. Comparing the proposed model with He algorithm, dark channel prior and Retinex algorithm, experimental results show that the proposed algorithm is superior to the traditional methods visually and objectively.

Key words: color image dehazing, dark channel prior, CTV model, non-local operators, split Bregman algorithm

摘要： 鉴于暗原色先验算法能复原不同雾浓度和场景深度的图像，而基于非局部算子概念的NL-CTV（Non-Local Color Total Variation）模型能较好地保持图像边缘和纹理等特征，融合暗原色先验与NL-CTV模型，提出了一种新型单幅彩色图像去雾模型。通过暗原色先验得到精确的大气光强度和大气传输函数，然后推导包含大气光强度和大气传输函数的非局部能量泛函，再通过引入辅助变量和Bregman迭代参数，为其设计相应的快速split Bregman算法来求解该模型。将该算法与He算法、暗原色先验和Retinex算法的实验结果进行分析比较，从而验证了该模型不论从视觉上，还是客观数据上都要优于其他两种算法。

关键词: 彩色图像去雾, 暗原色先验, CTV模型, 非局部算子, split Bregman算法