Defogging algorithm of underground coal mine image based on adaptive dual-channel prior
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摘要: 针对暗通道先验算法在处理煤矿井下图像时存在的图像失真、细节不足和图像暗光等问题，提出了一种基于自适应双通道先验的煤矿井下图像去雾算法。首先，根据大气散射物理模型与煤矿井下特殊环境，建立了煤矿井下尘雾图像退化模型。然后，融合暗通道与亮通道建立双通道先验模型来优化透射率，并加入自适应权重系数来提高透射率图的精度，采用梯度导向滤波代替传统导向滤波对透射率图进行细化处理。最后，结合矿井环境改进大气光值求取方法，根据尘雾图像退化模型复原图像。实验结果表明：该算法能够有效去除图像中的尘雾现象，避免了光晕模糊和过增强现象；相较于暗通道先验算法、Retinex算法、Tarel算法，该算法大幅提升了图像信息熵与平均梯度，使复原后图像的细节信息更加丰富，同时缩短了运行时间。Abstract: When dark channel prior algorithm is used to deal with underground coal mine images, there are problems of image distortion, lack of details and dark light. In order to solve the above problems, a defogging algorithm of underground coal mine image based on adaptive dual-channel prior is proposed. Firstly, according to the physical model of atmospheric scattering and the special environment of underground coal mine, the dust and fog image degradation model in underground coal mine is established. Secondly, a dual-channel prior model is established by fusing the dark channel and the bright channel to optimize the transmittance. An adaptive weight coefficient is added to improve the precision of the transmittance image. And the gradient guided filtering is adopted to replace the traditional guided filtering to refine the transmittance image. Finally, combined with the mine environment, the atmospheric light value calculation method is improved. And the image is restored according to the dust and fog image degradation model. The experimental results show that the algorithm can effectively remove the fog phenomenon in the image, avoid the halo blur and over-enhancement phenomenon. Compared with dark channel prior algorithm, Retinex algorithm and Tarel algorithm, this algorithm greatly improves the image information entropy and average gradient. The algorithm enriches the detailed information of the restored image and shortens the running time.
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图 1 基于自适应双通道先验的煤矿井下图像去雾算法流程
Figure 1. Flow of defogging algorithm for underground coal mine image based on adaptive dual-channel prior
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图 2 不同算法求出的透射率图对比
Figure 2. Comparison of transmittance graphs obtained by different algorithms
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图 3 不同算法得到的煤矿井下图像去雾结果对比
Figure 3. Comparison of image defogging results of underground coal mine obtained by different algorithms
表 1 不同算法去雾图像指标比较
Table 1 Indicators comparison of defogging images processed by different algorithms
图像 评价指标 本文算法 暗通道先验算法 Retinex算法 Tarel算法 图像1 信息熵 7.19 6.58 7.41 6.92 标准差 46.88 34.35 47.63 35.12 平均梯度 0.1342 0.0576 0.0969 0.0781 图像2 信息熵 7.34 6.69 7.48 7.00 标准差 48.96 35.77 48.52 37.46 平均梯度 0.1071 0.0487 0.0961 0.0682 图像3 信息熵 7.47 6.80 7.58 7.09 标准差 43.94 34.31 46.98 36.13 平均梯度 0.0906 0.0449 0.0770 0.0531 图像4 信息熵 7.05 6.36 7.46 6.81 标准差 53.69 36.97 47.48 32.11 平均梯度 0.0670 0.0313 0.0757 0.0550 图像5 信息熵 7.34 7.55 7.69 7.42 标准差 58.73 56.39 58.63 57.51 平均梯度 0.0734 0.0357 0.0633 0.0362 
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表 2 不同算法运行时间比较
Table 2 Comparison of running time of different algorithms s
图像 本文算法 暗通道先验算法 Retinex算法 Tarel算法 图像1 3.68 6.75 1.34 379 图像2 3.38 6.85 1.35 283 图像3 2.18 6.58 1.36 316 图像4 2.59 6.69 1.32 293 图像5 5.68 8.21 2.56 386
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