College of Mechanical Engineering, Xi'an University of Science and Technology
Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control

煤矿综采工作面空间小、照明环境复杂多变，采煤过程中伴随着大量的粉尘、大雾，导致采集的图像出现曝光、细节特征减弱等问题，难以对井下照明区域光照强度过大的图像进行有效的特征提取。针对上述问题，提出了一种煤矿井下非均匀照度图像去噪算法。首先，将视频截取为图像，判断图像是否需要进行光照抑制，将需要进行光照抑制的RGB图像拆分通道，并计算每个通道的光照调节因子，实现图像的整体光照调节；然后，将未进行整体光照抑制的图像和经整体光照抑制的图像进行反射分量提取，即将输入的图像转换为HSV空间图像，使用单尺度Retinex（SSR）算法对V通道图像中的光照分量进行单独处理，将V分量中的入射分量去除，保留反射分量，并对反射分量使用直方图均衡算法实现光照均衡化处理；最后，使用基于引导滤波的暗通道先验算法对经过光照处理后的图像进行去雾处理，并使用伽马校正函数重新调节亮度不均的图像。主观评价结果表明：提出的煤矿井下非均匀照度图像去噪算法有效抑制了因光照导致整体亮度较高的问题，且由于大雾、粉尘等因素导致图像模糊的部分更加清晰，图像的细节特征更加突出。采用信息熵、均值 、标准差 、空间频率4种评价指标对提出的算法效果进行客观评价，结果表明，提出的算法在信息熵、均值 、标准差 、空间频率上较多尺度Retinex（MSR）算法分别平均提升了21.87%，−56.06%，153.43%，294.45%，较基于颜色保持的多尺度视网膜增强（MSRCP）算法分别平均提升了1.18%，−39.56%，33.29%，−4.71%，较带色彩恢复的多尺度视网膜增强（MSRCR）算法分别平均提升了38.06%，−55.27%，462.10%，300.96%，说明提出的算法能更有效地增加图像信息量、抑制光照强度、提升边缘信息及图像清晰度。

The space of the fully mechanized working face is small, and the lighting environment is complex and variable. During the coal mining process, there is a large amount of dust and fog, which leads to problems such as exposure and weakened detail features in the collected images. It is difficult to effectively extract features from images with excessive lighting intensity in the underground lighting area. In order to solve the above problems, a denoising algorithm for uneven lighting images in coal mines is proposed. Firstly, the video is captured as an image to determine whether lighting suppression is necessary. The RGB image that requires lighting suppression is split into channels, and the lighting adjustment factor for each channel is calculated to achieve overall lighting adjustment of the image. Secondly, the images that have not undergone overall lighting suppression and those that have undergone overall lighting suppression are subjected to reflection component extraction. The input image is converted into an HSV spatial image, and the single scale Retinex (SSR) algorithm is used to separately process the illumination component in the V channel image. The incident component in the V component is removed, while the reflection component is retained. The histogram equalization algorithm is used to achieve illumination equalization for the reflection component. Finally, a dark channel prior algorithm with guided filtering is used to defog the light-processed image. The gamma correction function is used to readjust the image with uneven brightness. The subjective evaluation results indicate that the proposed denoising algorithm for uneven lighting images in coal mines effectively suppresses the problem of high overall brightness caused by lighting. The blurry parts of the original image are clearer due to factors such as fog and dust, and the detailed features of the image are more prominent. The effectiveness of the proposed algorithm is objectively evaluated using four evaluation indicators: information entropy, mean, standard deviation, and spatial frequency. The results showed that the proposed algorithm has achieved an average improvement of 21.87%, −56.06%, 153.43%, and 294.45% in terms of information entropy, mean, standard deviation, and spatial frequency compared to the multi-scale Retinex (MSR) algorithm. The proposed algorithm has achieved an average improvement of 1.18%, −39.56%, 33.29%, and −4.71% compared to the multi-scale Retinex with color preservation (MSRCR) algorithm. The proposed algorithm has achieved an average improvement of 38.06%, −55.27%, 462.10%, and 300.96% compared to the multi-scale Retinex with color restoration (MSRCR) algorithm. The results indicate that the proposed algorithm can more effectively increase image information, suppress lighting intensity, improve edge information, and image clarity.

国家自然科学基金青年项目(52104166)；中国博士后科学基金面上项目(2022MD723826)；陕西省重点研发计划项目(2023-YBGY-063)。

张旭辉，麻兵，杨文娟，等. 煤矿井下非均匀照度图像去噪研究[J]. 工矿自动化，2024，50（2）：1-8.

ZHANG Xuhui, MA Bing, YANG Wenjuan, et al. Research on denoising of uneven lighting images in coal mine underground[J]. Journal of Mine Automation，2024，50（2）：1-8.