目的 图像去雾领域越来重视去雾过程中对图像边缘细节的恢复和保护，针对现有主流的基于模型的有雾图像复原算法，基本都是对介质透射率进行直接求解，即先预估透射率，再细化抠图，运算量很大的问题，提出利用双边滤波方式进行介质透射率的间接求解，用以简化去雾算法时间复杂度。方法 利用双边滤波方式对介质透射率的求解，是先估算出较精确的大气散射函数及大气光值，然后间接求出透射率，其避免了采用软件抠图的方式对介质透射率进行细化的过程，提高了算法的时效性。结果 选取两组户外有雾图像进行实验，并从得出的透射率图、复原效果及运算时间进行了对比分析。本文算法能得到较为清晰的透射率分布图，并且改善了预估透射率图中的块状现象；本文算法对透射率细化的同时，还起到了平滑图像边缘的效果；耗时方面，本文算法对大小为608×456像素的图像恢复耗时为1.803 s。结论 本文算法对有雾图像进行全局清晰化处理的同时，重点恢复有雾图像的局部细节，复原结果能更好地保持图像边缘的效果，更适合运用到基于图像检测类的系统中去。

Objective It is critical to restore and preserve edge details in image defogging. In existing classical model-based defogging methods, medium transmittance is computed directly, i.e., image matting refinement follows transmittance estimation, so the computational complexity is high. In order to reduce time complexity, transmittance is indirectly computed in virtue of bilateral filtering. Method In the solution of medium transmittance based on bilateral filtering, atmospheric scattering function and light value are relatively accurately estimated; therefore, transmittance can be indirectly computed. The proposed algorithm avoids the image software matting for medium transmittance refinement, so real-time performance is improved. Result The experiments are performed on two sets of outdoor foggy image to compare transmittance map, restoration effect, and operation time. In the proposed method, transmission distribution map is clearer, and the block effect of transmittance estimation is restricted. Image edges are smoothed during transmittance refinement. The operation time of the proposed algorithm is just 1.803 second for the image with the size of 608×456. Conclusion In the global enhancement of foggy image of this paper, the local details are restored and the edges are preserved. Consequently, the proposed algorithm is suitable for image detection systems. The experiment results show that the proposed algorithm can protect edge details besides improving time efficiency.