Abstract: To address the issues of dark images, detail loss, and over-enhancement in existing underground dust and mist image clarification algorithms, an image clarification algorithm based on enhanced grid networks was proposed. This algorithm consisted of three parts: a preprocessing module, a backbone module, and an output module. The preprocessing module generated a set of feature maps using the feature extraction module IRDB, which served as the input for the backbone module. The IRDB integrated the advantages of the Inception architecture and the Residual Dense Block (RDB), increasing the depth and width of the network under limited resources, thereby enhancing the network's representational ability, generalization capability, and handling of dust and mist at different scales. The backbone module employed a grid network to further extract features at various scales of the image and implemented transformations of feature maps at different scales through upsampling and downsampling. To better capture detailed information in the images, a channel attention mechanism was introduced within the grid network. Experimental results indicated that with 5 IRDB modules, the network model achieved the best Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Naturalness Image Quality Evaluator (NIQE) metrics. Visually, the images processed using the proposed algorithm exhibited richer detail information, more natural colors, and improved clarity and contrast. The PSNR, SSIM, and NIQE values for the images processed by the proposed algorithm on the underground dataset were 23.69, 0.8401, and 8.95, respectively, with a moderate image processing speed, and the overall performance surpassed similar algorithms such as DCP and AOD-Net.