基于两次双边滤波的煤矿监控图像去雾研究

Research on Coal Cine Conitoring Image Based on Twice Bilateral Filter

煤矿井下智能视频监控系统在各大煤矿得到广泛的应用,然而由于井下条件恶劣,视频监控中经常遇到伴有各种随机噪声的尘雾图像。针对降质严重、视觉效果差的监控图像,提出一种基于两次双边滤波的快速图像去雾方法(FDA-DBA)。首先利用四叉树方法获取全局环境光亮度,然后对最小通道图采用双边滤波器获取粗略的大气散射图并进一步优化;其次利用容差机制进行透射率修正,解决明亮区域处理后颜色失真问题;最后利用大气散射模型复原雾尘图像。实验表明,该算法能较准确地恢复场景的色彩和清晰度,可获得较真实的清晰无雾图像,具有较高的准确性和鲁棒性,并且算法的时间复杂度与图像像素数呈线性函数,适合于煤矿智能视频监控环境。

Coal mine intelligent video monitoring system in the coal mine are widely used.However,due to the harsh down hole conditions,video surveillance frequently encountered accompanied by a variety of random noise of fog image.In view of the serious degradation and poor visual effect of the monitoring image,a fast image defog method based on the twice bilateral filtering(FDA-DBA)is proposed.First getting the environment brightness by quadtree,and then using the bilateral filter towards minimum channel map to obtain coarse scattering map and do further optimization to get high precise transmittance map,secondly using the tolerance mechanism to correct the transmission to get high precise transmission map and resolve color distortion in the bright area after dehazing,finally restore the fog degraded image based on the atmospheric scattering model.Experiments show that the algorithm in this paper can accurately recover the scene color and clarity,and get a real clear no-fog image,and has higher accuracy and robustness,what’s more,the relationship between the time complexity of this algorithm and the number of image pixels is a linear correlation,which makes the algorithm in this paper is suitable for the environment of coalmine intelligent video surveillance.