Abstract: When the dark channel image dehazing algorithms deal with the bright region without satisfying the dark channel fog priori condition, the estimated transmission is relatively small, and it leads to large deviation from the original image in terms of color, smoothness and texture. Therefore, a feedback regulation mechanism of the dark channel dehazing was proposed. First, removed haze using dark channel prior algorithm and gave the feedback difference of the texture smoothness of haze-free image and the original image, segmented the bright region by using Fuzzy C-Means (FCM) algorithm, and then used the Gaussian function to adjust the transmission of the bright region, made it closer to the actual transmission. Finally, the article got haze-free image by using the adjusted transmission. The experimental results show that the proposed algorithm can effectively deal with the bright region which does not meet the assumptions of dark channel. It also makes the dehazed image's color more accord with the real scene, and its visual effect is also better. This method can improve the robustness of outdoor surveillance system.

Key words: dehazing, dark channel priori, Fuzzy C-Means (FCM) clustering, texture smoothness, feedback regulation

摘要： 针对暗通道图像去雾算法在处理不满足暗通道先验条件的明亮区域时，估计的透射率偏小，导致去雾后的图像与原图像相比，色彩和纹理平滑度出现较大偏差的问题，提出反馈调节的暗通道去雾算法。该算法首先通过暗通道算法对原始有雾图像进行去雾，反馈出去雾后的图像与原始图像纹理平滑度的差异，使用模糊C-均值聚类算法分割出明亮的区域；然后用高斯函数调整明亮区域偏小的透射率，使其更加接近实际的透射率；最后利用调整后的透射率求得清晰的无雾图像。实验结果表明，该算法可以有效地处理不满足暗通道先验条件的区域，使得包含明亮区域的雾化图像，去雾后的色彩更加符合真实场景，视觉效果也更好。该算法可以提高户外监视系统的鲁棒性。

关键词: 图像去雾, 暗通道先验, 模糊C-均值聚类, 纹理平滑度, 反馈调节