针对暗通道先验在天空区域的失效问题,提出了一种基于亮度模型融合的改进暗通道先验图像去雾算法。首先通过Canny算子分割得到天空区域与非天空区域;其次,利用亮度模拟景深,重构亮度透射率,并通过与暗通道透射率的融合构成天空区域透射率,最后的透射率图经由快速引导滤波进行精细化处理;大气光值选择抗干扰能力更强的天空区域中像素强度值前0.1%的像素中值;最后,经由大气散射模型恢复出无雾图像。实验结果表明,该算法针对含雾图像能够有效地恢复出图像的细节并抑制光晕现象,明亮度适宜,颜色自然。

An improved dark channel prior image dehazing algorithm based on the fusion luminance model was proposed to deal with the failure of a dark channel prior in the sky. First, sky and non-sky areas were segmented using a Canny operator. Next, luminance transmission was constructed by simulating the depth of the scene using the luminance, which combined with the transmission of the dark channel to form the transmission of the sky area. A fast-guided filter was used to optimize the transmission map. The value of atmospheric light was selected as the median of the top 0.1% of the pixels with strong anti-interference ability. Finally, the haze-free image was restored using the atmospheric scattering model. Experimental results show that the algorithm could effectively recover the details of the image and suppress the halo phenomenon for the haze image including the sky area with appropriate brightness and natural color.