摘要： 利用暗通道先验去雾算法处理包含大块亮白区域的图像时，存在图像失真的问题。为此，提出一种改进的的单幅图像去雾算法。利用基于图像子块平均灰度值和标准差的四叉树分解方法得到大气光估值，通过图像暗通道直方图分布自适应计算分割阈值，据此将图像划分为亮白区域与非亮白区域。根据图像的灰度分布数据计算权重因子，将其融入透射率以提高图像边缘的平滑度。在此基础上，应用大气散射模型恢复无雾图像。实验结果表明，该算法能够有效解决天空区域色彩失真的问题，所得图像视觉效果明亮自然，图像交界景深突变处也较为平滑。

关键词: 图像去雾, 阈值分割, 四叉树分解, 大气散射模型, 引导滤波

Abstract: When the dark channel priori dehazing algorithm is used to process an image with a large bright white area,the problem of image distortion occurs.To this end,an improved single image dehazing algorithm is proposed.The atmospheric light is obtained by quad-tree decomposition method based on sub-block average gray value and standard deviation.The segmentation threshold is adaptively calculated by the image dark channel histogram distribution,and the image is divided into a bright area and a non-bright area.The grayscale distribution of the image is used to calculate a weighting factor.And this weighting factor is used to blend the transmittance to make the edges smoother.On this basis,the haze-free image is restored by the atmospheric scattering model.Experimental results show that the algorithm effectively solves the problem of color distortion in the sky region,the visual effect is bright and natural,and the sudden change of the depth of the image boundary is smooth.

Key words: image dehazing, threshold segmentation, quad-tree decomposition, atmospheric scattering model, guided filtering

中图分类号: 

• TP751