基于融合亮度模型和梯度域滤波的图像去雾#br#

为解决暗通道先验算法在处理图像的天空区域时容易出现颜色过饱和、亮度整体偏暗和光晕等问题,提出了一种融合亮度模型和梯度域滤波的图像去雾算法.首先,选择整幅图像中亮度最大的前0.1％ 像素的平均值作为大气光值;然后,利用自适应最小值滤波的改进暗通道模型和亮度模型分别对前景区域和天空区域求解透射率,在将其加权融合得到粗透射率的基础上,使用梯度域导向滤波对透射率进行细化;最后,通过大气散射模型和伽马校正复原出无雾图像.实验结果表明,在包含天空区域的雾图上本文算法能够快速有效地解决天空区域的光晕效应和图像失真问题,复原出来的图像清晰自然,保留了较多的细节信息,在主观和客观2个评价方面均优于其他对比算法.

Image dehazing based on fusion luminance model and gradient domain filter

In order to solve the problems of the dark channel prior algorithm, such as color oversaturation, overall luminance darkening and halo in the sky, an image dehazing algorithm fusing the luminance model and gradient domain filter is proposed. Firstly, the average value of the first 0.1% pixels with the largest luminance is selected as the atmospheric light value. Secondly, the improved dark channel model and luminance model of adaptive minimum filter are used to solve the transmission in the foreground region and the sky region respectively, and then the rough transmission is obtained by weighted fusion. On the basis of that, the gradient domain guided filter is used to refine the transmission. Finally, the haze-free image is restored by atmospheric scattering model and gamma correction. The experimental results show that this algorithm can quickly and effectively solve the halo effect and image distortion of the sky region on the haze image containing the sky region, and the restored image is clear and natural, with more detailed information retained, which is better than other comparison algorithms in subjective and objective evaluation.