摘要
提出了一种基于迭代的自适应图像去雾算法,该算法是在基于暗通道先验理论的去雾算法基础上增加了常数c补偿,为了保护边缘信息滤波时采取了双边滤波算法,最后将大气光及透射率当成一个整体并结合峰值信噪比构造了优化条件的迭代算法,实现了自适应图像去雾。模拟实验结果与多种评价性能参数的计算结果都表明,本文的改进算法不仅提高了去雾后图像的清晰度和对比度,而且迭代速度快、图像性能好。
An iterative adaptive image de-fogging algorithm is proposed.The constant c compensation is added to the de-fogging algorithm based on dark channel prior theory.In order to protect edge information,bilateral filtering is adopted.Finally,the iterative algorithm of optimization condition is constructed with atmospheric light and transmittance as a whole and peak signal-to-noise ratio.The simulation results and the calculation results of various evaluation performance parameters show that the improved algorithm not only improves the clarity and contrast of the image after fog removal,and also has fast iteration speed and good image performance.
作者
车雯雯
李竹林
徐雪丽
CHE Wen-wen;LI Zhu-lin;XU Xue-li(School of Mathematics and Computer Science,Yan′an University,Yan′an 716000,China)
出处
《延安大学学报（自然科学版）》
2021年第1期72-77,共6页
Journal of Yan'an University：Natural Science Edition
基金
延安大学校级科研计划项目(YDQ2019-10)。
关键词
图像去雾
暗通道先验理论
常数补偿
迭代算法
image fogging
apriori theory of dark channel
constant compensation
iterated algorithm
