基于暗通道先验去雾算法的优化

Optimization of a dehazing algorithm based on dark channel prior

在雾霾环境下获取的图像往往是不清晰的,研究人员提出了很多去雾算法以获得清晰图像。在暗通道先验去雾算法的基础上,针对现有算法在处理户外有雾图像后存在色彩偏移以及在景深突变区域出现光晕现象提出改进。首先分析之前一些经典算法易错选大气光值的原因,为此采取基于四叉树递归思想,最终选取一定大小的局部区域像素点平均值作为大气光值。接着分析了在景深突变区域产生光晕的原因提出了在估算透射率时,采用分区域最小值滤波,直接获得精细的透射率值,避免额外细化透射率的滤波算法,提升算法效率,最后结合大气耗散模型,利用改进优化后算法获得的大气光值和透射率图恢复去雾后图像。实验结果表明,改进算法不但能较好较快的去雾后效果图,并且在色彩还原和细节还原取得了很好效果,最后与其他几种经典的图像去雾算法分别进行客观与主观对比,证实了算法的可靠性。

Images acquired in a hazy environment are often unclear,and researchers have proposed many defogging algorithms to obtain clear images.On the basis of the dark channel prior defogging algorithm,the existing algorithm has improved the color shift after processing outdoor foggy images and the halo phenomenon in the sudden change of depth of field.To this end,firstly analyze the reasons why some classical algorithms are easy to choose the atmospheric light value.In this paper,based on the recursive idea of quadtree,the average value of the local area pixel is selected as the atmospheric light value.Then the reason for the generation of Halo in the sudden change of depth of field is analyzed.When estimating the transmittance,the sub-region minimum filter is used to directly obtain the fine transmittance value,which avoids the additional filtering algorithm for refining the transmittance and improves the efficiency of the algorithm.Finally,combined with the atmospheric dissipation model,the image after de-fogging is restored by using the optimized atmospheric light value and transmittance map.The experimental results show that the improved algorithm not only has a better post-fog effect map,but also achieves good results in color reproduction and detail reduction.It is objective and subjective comparison with several typical image dehazing algorithms,which proves the algorithm is feasibility.