基于暗通道补偿与大气光值改进的图像去雾方法

Image Dehazing Method Based on Dark Channel Compensation and Improvement of Atmospheric Light Value

针对暗通道先验去雾算法存在的光晕现象、大气光值选取不准确等问题,提出了一种基于暗通道补偿与大气光值改进的图像去雾方法。为减弱图像景物边缘处的光晕效应,提出了一种基于暗通道补偿模型的解决办法,利用加权通道差值的方法识别光晕区域,通过腐蚀、融合等处理修正该区域的暗通道值,采用线性融合的方式与原暗通道进行融合,实现对暗通道的补偿。针对大气光值选取不准确的问题,改进了四叉树分割方法,即增加相邻区域比较的策略,使该算法可以更加精确地获取大气光值,使恢复后的图像更加清晰自然,细节保留更加丰富。借助大气散射模型与优化后的透射率恢复无雾图像。实验结果表明,本文方法能够有效地去除光晕效应,准确地获取大气光值。

Aiming at the problems of the halo phenomenon and inaccurate selection of atmospheric light values in dark channel prior algorithm,an image dehazing method based on dark channel compensation and improvement of atmospheric light value is proposed in this paper.In order to weaken the halo effect at the edge of the image scene,a solution based on the dark channel compensation model is proposed first,the halo region is identified by the weighted channel difference method,and then the dark channel values of this region are modified by corrosion,fusion,and other treatment.It is linearly fused with the original dark channel images to compensate the dark channel.For the problem of inaccurate selection of atmospheric light value,the quadtree segmentation method is improved,with the strategy of adjacent region comparison added.Hence,the proposed method can obtain more accurate atmospheric light values,leading to more clear and natural restored images with more details.Finally,the haze-free image is restored by means of the atmospheric scattering model and the optimized transmittance.The experimental results show that the proposed method can effectively remove the halo effect and obtain the atmospheric light value accurately.