暗原色先验与NL-CTV模型相结合的图像去雾方法  

Image dehazing method based on dark channel prior and NL-CTV model

作　　者：赵胜楠 魏伟波 潘振宽 李帅 ZHAO Shengnan;WEI Weibo;PAN Zhenkuan;LI Shuai(School of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China)

机构地区：[1]青岛大学计算机科学技术学院,山东青岛266071

出　　处：《计算机工程与应用》2018年第16期182-186,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61170106);山东省高等学校科技计划项目(No.J14LN39)

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。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.

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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]