基于雾线暗通道先验改进的图像去雾算法

Improved Image Dehazing Algorithm Based on Haze-line and Dark Channel Prior

针对现有图像去雾算法中大气光值和透射率估计不准确导致图像去雾后失真的问题,提出了一种基于雾线暗通道先验改进的图像去雾算法。首先,根据HSV空间雾浓度与亮度和饱和度差值的关系计算图像的全局相对雾浓度,并结合暗通道图对应的高像素值来设置能够自动选择合适的大气光值的权重系数;其次,利用暗通道先验得到的粗略透射率值对每条雾线中最大半径透射率进行修正,然后引入容差参数对明亮像素的透射率进行优化,引入快速引导滤波对透射率图进行进一步优化;最后,根据大气散射模型获得最终的无雾图像。实验结果表明,所提去雾算法在主观视觉效果和客观数据上均优于其他算法。

To address the problem of image dehazing distortion caused by inaccurate estimation of atmospheric light value and transmission in existing image dehazing algorithms,an improved image dehazing algorithm based on dark channel prior and hazeline prior is proposed.First,we compute the global relative haze concentration of the image using the relationship between haze concentration and the difference in brightness and saturation in HSV space and combine the highpixel value corresponding to the dark channel map to set the weight coefficient that can automatically select the appropriate atmospheric light value.Second,we use the rough transmittance value obtained by the dark channel prior to correct the maximum radius transmittance in each hazeline,and then introduce a tolerance parameter to optimize the transmittance of bright pixels.Next,fast guiding filtering is introduced to further optimize the transmittance maps.Finally,the final hazefree image based on the atmospheric scattering model is obtained.The experimental results show that the image dehazing algorithm proposed in this research outperforms the current algorithms in terms of subjective visual effect and objective data.