A defogging algorithm for coal mine underground images based on dark channel guided filtering and lighting correction
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摘要: 现有煤矿井下图像去雾方法在处理煤矿井下图像时未能在提取图像深层次特征信息的同时进行光照校正，处理后的图像存在细节信息丢失或图像偏暗的问题。提出一种基于暗通道引导滤波和光照校正的煤矿井下图像去雾算法。首先，井下原始图像通过图像分化模块（IDM）进行双边滤波、光照估计和暗原色处理后得到光照图、暗原色图和光照反射图。然后，对暗原色图进行预处理，作为权重引导参数对光照反射图进行引导滤波，以恢复图像细节特征信息。最后，将光照图作为权重参数对图像进行光照校正和特征提取，通过多次光照校正解决颜色失真问题，同时增加网络深度，进而去除黑暗区域的退化，实现图像细节的重构，从而得到清晰图像。主观评价结果表明：基于暗通道引导滤波和光照校正的煤矿井下图像去雾算法在去除雾气的同时，保留了更多的结构纹理及背景细节，使整个图像更加接近于对应的清晰图像。客观评价结果表明：与次优算法PMS−Net相比，在训练集和测试集上信息熵分别提高0.32和0.11，标准差分别提高3.58和1.89，平均梯度分别提高0.008和0.004，说明所提算法可有效降低煤矿井下图像的雾气。消融实验结果表明，所提算法在测试数据集上的信息熵、标准差、平均梯度均高于其他网络组成模型，说明所提算法去雾效果最好，且能有效保留图像细节和边缘信息。Abstract: The existing methods for defogging coal mine images fail to perform lighting correction while extracting deep level feature information, resulting in the loss of detail information or image darkening in the processed images. A defogging algorithm for coal mine underground images based on dark channel guided filtering and lighting correction is proposed. Firstly, the original underground images are subjected to bilateral filtering, lighting estimation, and dark primary color processing using the image differentiation module (IDM) to obtain lighting maps, dark primary color maps, and lighting reflection maps. Secondly, the method preprocesses the dark primary color map and uses it as a weight guidance parameter to guide the filtering of the lighting reflection map, in order to restore the image's detailed feature information. Finally, the lighting map is used as a weight parameter to perform lighting correction and feature extraction on the image. The color distortion problem is solved through multiple lighting corrections, while increasing the network depth to remove degradation in dark areas, achieving reconstruction of image details and obtaining clear images. The subjective evaluation results indicate that the coal mine underground image defogging algorithm based on dark channel guided filtering and lighting correction retains more structural textures and background details while removing fog. It makes the entire image closer to the corresponding clear image. The objective evaluation results show that compared with the suboptimal algorithm PMS-Net, the information entropy on the training and testing sets is increased by 0.32 and 0.11, the standard deviation is increased by 3.58 and 1.89, and the average gradient is increased by 0.008 and 0.004, respectively. This indicates that the proposed algorithm can effectively reduce the fog in coal mine underground images. The results of ablation experiments show that the proposed algorithm has higher information entropy, standard deviation, and average gradient on the test dataset than other network models. It indicates that the defogging effect is the best and it can effectively preserve image details and edge information.
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图 6 不同算法单幅图像的处理速度对比
Figure 6. Comparison of processing speeds for single images using different algorithms
表 1 不同算法客观评价结果
Table 1. Objective evaluation results of different algorithms
算法 训练集 测试集 信息熵 标准差 平均梯度 信息熵 标准差 平均梯度 DCP 5.56 35.89 0.055 5.39 32.69 0.052 PMS−Net 7.55 54.76 0.124 7.32 52.86 0.120 CEEF 6.45 48.86 0.073 6.02 45.39 0.068 FFA 6.14 44.67 0.062 5.59 40.42 0.055 本文算法 7.87 58.34 0.132 7.43 54.75 0.124 
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表 2 消融实验结果
Table 2. Results of ablation experiments
模型 信息熵 标准差 平均梯度 w/o IDM 4.23 40.74 0.063 w/o BFM 5.78 48.94 0.073 w/o LCM 5,27 45.86 0.078 完整网络 7.43 54.75 0.124
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