【摘要】 由于煤矿井下地形环境复杂、光照受限，视频监控设备获取的图像常存在亮度不足、对比度低、颜色失真、细节信息丢失等问题.针对上述问题，提出一种基于MCGN(multiscale calibrated gating network，多尺度校准门控网络)煤矿井下多场景低光照图像增强算法，该算法由光照增强网络、细节增强网络、色彩矫正网络以及门控融合网络组成.首先，光照增强网络通过预点亮模块估计光照信息，在此基础上，级联具备空间增强注意力的光照增强模块，增强对遮挡区域和局部暗区的捕捉能力，随后引入自校准模块进一步提升图像整体曝光控制能力.其次，为丰富增强图像中纹理和边缘细节信息，设计多级残差结构构成细节增强网络，确保重要细节信息不丢失.再者，针对图像固有的以及增强过程中产生的颜色失真现象，构建色彩矫正网络和色彩损失函数，利用编解码结构将彩色图像解耦为颜色直方图，基于颜色直方图学习自然光颜色特征，以指导颜色分布矫正.最后，为实现三个网络输出图像有机融合，门控融合网络设计了一种新的门控机制，端到端学习最优融合权重，实现亮度增强、细节恢复和颜色矫正的有效平衡.实验结果表明，该算法在提升图像亮度、丰富纹理特征、还原真实色彩方面成效卓著.同时，该算法具有良好的多场景适用性和较快的推理速度，能满足煤矿井下实际需求，为煤矿安全生产提供有力的技术支撑.更多还原

【Abstract】 Due to the complex terrain environment and limited illumination in coal mines, images acquired by video surveillance equipment often have problems such as insufficient brightness, low contrast, color distortion, and loss of detail information. To solve the above problems, a multi-scene low-light image enhancement algorithm of a coal mine based on MCGN(multi-scale calibrated gating network) is proposed. The algorithm is composed of illumination enhancement network, detail enhancement network, color correction network, and gating fusion network. Firstly, the illumination enhancement network estimates the illumination information through a pre-lighting module. On this basis, the illumination enhancement module with spatial enhanced attention is cascading to enhance the capture ability of the occluded area and the local dark area. Subsequently, a self-calibration module is introduced to further improve the overall exposure control ability of the image. Secondly, to preserve and enhance texture and edge details, a multi-level residual structure is designed to form a detail enhancement network, ensuring that important detail information is not lost. Furthermore, in view of the inherent color distortion of the image and the color distortion generated in the enhancement process, a color correction network and a color loss function are constructed. The codec structure is used to decouple the color image into a color histogram, and the natural light color characteristics are learned based on the color histogram to guide the color distribution correction. Finally, in order to realize the organic fusion of the output images of the three networks, a new gating mechanism is designed in the gated fusion network, which learns the optimal fusion weights end-to-end to achieve an effective balance of brightness enhancement, detail restoration, and color correction. Experimental results show that the proposed algorithm is effective in improving image brightness, enriching texture features, and restoring true color. At the same time, the algorithm has good multi-scene applicability and fast reasoning speed, which can meet the actual needs of coal mines and provide strong technical support for coal mine safety production.更多还原