The dehazing algorithm based on dark channel priori theory is an effective method for dehazing of single image. However, it still has shortcomings in some of special cases. In this work, a method is proposed to impair these disadvantages. By considering local smoothness of the sky region, pixels in the bright regions (such as light source, white object, etc.) of the haze image are first excluded from being mistaken as reference pixels, so that the estimation of subsequent parameters is more accurate. Meanwhile, the pixels of the most occurrences are selected to replace the ones in the neighborhood centered on the edge pixels, and the operation is carried out pixel by pixel along the edge contour in replace of the minimum filter. This makes the estimation of transmission near the edge in better agreement with the actual situation, and can effectively avoid the “white haze” which is an artifact caused by the minimum filtering in the areas with the depth of field changing dramatically. Compared with the previous methods, the proposed algorithm in this paper can restore images more clearly, with more image edge details retained, and effectively improve visual effect of the scene in haze weather.

基于暗通道先验理论的去雾算法是一种有效的单图像去雾方法。但是，它在某些特殊情况下仍存在缺陷。在这项工作中，提出了一种减轻这些缺点的方法。通过考虑天空区域的局部平滑度，首先排除雾度图像的亮区域（例如光源，白色物体等）中的像素被误认为是参考像素，以便后续参数的估计更加准确。同时，选择出现次数最多的像素来替换以边缘像素为中心的邻域中的像素，并且沿着边缘轮廓逐个像素地进行操作以替换最小滤波器。这样可以估算边缘附近的传输情况，使其与实际情况更好地吻合，并可以有效避免“白雾”，该白雾是由景深变化剧烈的区域中的最小滤波引起的。与以前的方法相比，本文提出的算法可以更清晰地还原图像，保留更多的图像边缘细节，并有效地提高了雾霾天气场景的视觉效果。