The complex underground coal mine environment suffers from poor illumination, high humidity, and suspended dust-conditions that easily form water mist and glare. These factors lead to the loss of high-frequency information and blurring of edge details in captured images, while also superimposing noise interference. To improve mine image quality and address the challenge of synergistically suppressing noise and restoring details in mine scene super-resolution reconstruction, a mine image super-resolution reconstruction method based on multi-path adap-tive information enhancement is proposed. Methodologically, a Residual Multi-path Feature Aggregation Block (RMFAB) is designed first, leveraging residual learning and a Multi-path Adaptive Convolution Network (MACN) to fully utilize features from different paths, significantly enhancing the modeling capability for both global and local high-frequency information. Second, a Multi-attention Fusion Module is introduced to focus on high-frequency information across channel and spatial dimensions, improving feature representation. Finally, a Large Kernel Perception Block (LKPA) is constructed, employing multi-scale convolution to expand the receptive field and fuse hierarchical features, optimizing texture and structural details. Experimental results on the public CMUID mine dataset demonstrate that the proposed method outperforms existing state-of-the-art algorithms in both Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). Particularly at a scaling factor of 4, the algorithm achieves PSNR improvements of 2.88, 2.04, 1.94, 1.52, 0.53, 0.36 dB over Bicubic, CRAFT-SR, PAN, ESRGCNN, DiVANet, and SMAFNet, respectively. Corresponding SSIM improvements are 4.32%, 3.37%, 3.20%, 2.74%, 3.19%, 1.08%. The method achieves refined extraction and fusion of multi-level features in mine images, effectively suppressing noise interference while restoring complex texture features. This enhances the super-resolution reconstruction quality of mine images, thus contributing to intelligent perception in coal mine environments.
Qi et al. (Mon,) studied this question.