基于雾线和颜色衰减先验的图像去雾方法

Image dehazing method based on haze-line and color attenuation prior

针对现有图像去雾方法易产生的颜色过饱和、细节丢失、伪影等问题,提出了一种基于雾线和颜色衰减先验的去雾方法。首先,利用雾线先验和霍夫投票估计大气光。然后,根据颜色衰减先验建立关于场景深度的非线性模型,获取准确的透射率。最后,通过对大气散射模型进行反向求解去除图像中的雾霾干扰,获得细节丰富的去雾图像。在RESIDE公共数据集上进行了实验,并与多种现有方法进行了比较。实验结果表明,所提方法可有效去除图像中的雾霾干扰,获得清晰自然的去雾图像,且其时间和空间效率均优于其他方法。

To solve the problems caused by the existing image dehazing methods, such as color over-saturation, detail loss, and artifacts, a dehazing method was proposed based on haze-line and color attenuation prior. Firstly, haze-line prior and Hough vote were used to estimate the atmospheric light. Then, a nonlinear model on scene depth was constructed according to color attenuation prior theory to achieve transmission accurately. Finally, the haze was removed from the image by inversely solving the atmospheric scattering model, so as to obtain the dehazed image with rich details. The proposed method was tested on public RESIDE dataset in comparison with many existing methods. Experimental results show that the proposed method can effectively remove the haze from image and obtain a clear and natural dehazed image,which has higher time and space efficiency than other methods.