Abstract: Since the dark channel prior transmission and atmospheric light in the bright region are poorly estimated, an improved dehazing algorithm combined with atmospheric light and transmission was proposed. On the basis of analysis of the characteristics of Gaussian function, a preliminary transmission was estimated through the Gaussian function of dark channel prior of a fog image, and the maximum and minimum operations were used to eliminate the block effect. Next, the atmospheric light was obtained by atmospheric light description area, which was acquired by halo operation and morphological dilation operation. Finally, a clear image could be reconstructed according to the atmospheric scattering model. The experimental results show that the proposed algorithm can effectively remove the fog from the image and the recovered effect of thick fog is better than the comparison algorithms, such as dark channel prior, meanwhile the algorithm has a faster processing speed and is suitable for real-time applications.

Key words: Gaussian function, atmospheric scattering model, dehazing, atmospheric light area, Dark Channel Prior (DCP)

摘要： 针对暗原色先验透射率在明亮区域估计不足以及大气光误差问题，提出一种结合透射率和大气光改进的去雾算法。在分析高斯函数特点的基础上，依据有雾图像暗原色的高斯函数初步估计透射率，利用最大最小操作消除块状效应；然后，通过晕光算子与形态学膨胀操作获取大气光描述区域来获取大气光值；最后根据大气散射模型复原清晰图像。实验结果表明，所提算法能够有效去除图像中的雾气，浓雾图像恢复效果相比暗原色先验等算法更佳，且处理速度较快，便于实时应用。

关键词: 高斯函数, 大气散射模型, 去雾, 大气光区, 暗原色先验