煤矿井下环境复杂，受光照、煤尘、水雾的影响，采集的图像往往存在细节模糊、纹理缺失等问题，低分辨率的矿井图像对煤矿安全监控的智能化发展带来诸多制约。图像超分辨率重建作为一种重要的图像处理技术，旨在从矿井低分辨率图像中恢复出清晰的高分辨率图像，从而显著提升煤矿智能监测与安全管理的可靠性。针对矿井图像边缘纹理信息缺失、细节模糊不清等质量退化问题，笔者提出一种矿井图像的多维特征与残差注意力网络超分辨率重建方法。首先，采用多分支网络将动态卷积与通道注意力机制进行并行融合，以“水平−通道”“垂直−通道”交互方式来捕获不同的空间统计特性。其次，设计了一种递归稀疏自注意力机制，在线性复杂度下聚合代表性特征图，自适应选择权重分配，减少计算过程中的信息冗余。最后，基于标准多头自注意力机制和残差连接方式构建深层特征提取的基本单元，将获得的特征信息与浅层特征通过跳跃连接共同输入重建模块，完成超分辨率矿井图像重建。实验结果表明，笔者所提方法在客观评价指标和主观视觉分析上较现有主流算法均有明显提升。在矿井数据集的测试中，2倍和4倍缩放因子下的图像相似性（LPIPS）平均降低10.97%、9.91%，峰值信噪比（PSNR）平均提升4.10%、2.30%，证明了该方法在恢复矿井图像结构和纹理细节上的有效性。

The complex underground environment of coal coalmines, influenced by lighting, coal dust, and water mist, often results in collected images with blurred details and missing textures, leading to decreased image resolution and posing significant limitations to the intelligent development of coal coalmine safety monitoring. Image super-resolution reconstruction, an essential image processing technology, aims to recover clear high-resolution images from low-resolution coalmine images, thereby significantly enhancing the reliability of intelligent monitoring and safety management in coal coalmines. To address issues such as the loss of edge texture information and blurring of details in coalmine images, a coalmine image super-resolution reconstruction method integrating multi-dimensional features and residual attention networks is proposed. First, a multi-branch network is employed to parallelly integrate dynamic convolution and channel attention mechanisms, capturing different spatial statistical characteristics through "horizontal-channel" and "vertical-channel" interactions. Secondly, a recursive sparse self-attention mechanism is designed to aggregate representative feature maps under linear complexity, adaptively selecting weight distribution and reducing information redundancy during computation. Finally, the basic unit of deep feature extraction is constructed based on the standard multi-head self-attention mechanism and residual connection, with the obtained feature information and shallow features jointly input into the reconstruction module via skip connections to complete super-resolution reconstruction of coalmine images. Experimental results indicate that the proposed method significantly outperforms existing mainstream algorithms in both objective evaluation metrics and subjective visual analysis. In tests on the coalmine dataset, LPIPS (Learned Perceptual Image Patch Similarity) decreases by an average of 10.97% and 9.91%, while PSNR (Peak Signal-to-Noise Ratio) increases by an average of 4.10% and 2.30% for 2x and 4x scaling factors, respectively, demonstrating the method's effectiveness in restoring the structure and texture details of coalmine images.