摘要： 煤矿井下复杂光照环境导致图像存在对比度低、细节模糊的情况，现有的图像增强算法存在特征捕捉能力不够全面，且针对不同层次的语义特征之间融合方式低效等问题，本文提出了融合卷积和MLLA的煤矿井下图像增强方法（ICM），在卷积阶段堆叠了多个具有退化感知的混合专家模块，使模型能够自适应恢复在图像增强过程中由于丢失的局部纹理细节，解决伪影、细节特征不清晰的问题。使用具有背景感知能力的MLLA（Mamba Like Linear Attention）模块对图像中的长期依赖关系进行建模来提高输出增强图像的全局结构一致性和改善纹理保真度。引入交互式融合分支以编码主干特征和重建特征之间的阶段相关性，有效利用局部和全局特征辅助图像增强效果。分段损失函数在不同增强阶段设置不同的损失目标，使得网络能够在每个阶段自适应地优化。与近期表现优秀的深度学习方法对比，ICM方法在评价指标PSNR、SSIM、NIQE和LPIPS展现出最佳效果，分别为30.524dB、0.946、3.06和0.23，能够有效地提升煤矿井下低照度图像的亮度、对比度和清晰度，为矿井安全监测与智能决策提供可靠视觉支持。

Abstract: The complex lighting environment underground in coal mines leads to low contrast and blurry details in images. Existing image enhancement algorithms have insufficient feature capture capabilities and inefficient fusion methods for semantic features at different levels. This paper proposes an underground coal mine image enhancement method (ICM) that combines convolution and MLLA (Mamba Like Linear Attention). In the convolution stage, multiple mixed expert modules with degradation perception are stacked to enable the model to adaptively restore local texture details lost during image enhancement, solving the problems of artifacts and unclear detail features. Using an MLLA module with background perception capability to model long-term dependencies in images to improve the global structural consistency and texture fidelity of output enhanced images. Introducing interactive fusion branches to encode the stage correlation between backbone features and reconstructed features, effectively utilizing local and global features to assist in image enhancement. The segmented loss function sets different loss objectives at different enhancement stages, enabling the network to adaptively optimize at each stage. Compared with recently excellent deep learning methods, the ICM method shows the best performance in evaluation metrics PSNR, SSIM, NIQE, and LPIPS, with values of 30.524dB, 0.946, 3.06, and 0.23, respectively. It can effectively improve the brightness, contrast, and clarity of low light images in coal mines, providing reliable visual support for mine safety monitoring and intelligent decision-making.