Abstract: Quantitative analysis of coal maceral is crucial for objectively evaluating the properties and quality of coal, thereby enabling its efficient utilization. However, due to irregularities in operation or broken tools, it is easy to cause the scratch of the polished grain mounts, which affects the further automatic analysis, and the re-making of the polished grain mounts will cause the waste of manpower and resources. In view of this, in order to repair scratch areas in coal photomicrographs, a deep learning-based photomicrographs scratch detection and repair strategy is proposed. Firstly, a dual-attention model combining spatial attention and channel attention is designed for scratch detection, which fully mines the semantic information of coal photomicrographs and integrates it with U-Net semantic segmentation network to improve the accuracy of scratch detection. Then, to address the issue of texture differences between the scratch-removed areas and surrounding areas in image repair algorithms based on patch matching, a coal photomicrographs restoration network combining contextual attention and generative adversarial learning is designed. The network adaptively fills the scratched area of coal with reasonable content and improves the quality of image restoration. Experimental results show that the average pixel accuracy and mean Intersection over Union for scratch detection reached 90.93% and 83.95%, respectively, while the peak signal-to-noise ratio and structural similarity (SSIM) of the repaired images reached 43.29 dB and 99.32%, respectively. Compared to traditional patch-matching-based algorithms, these represent improvements of 8.76 dB and 1.65%, respectively, verifying the effectiveness of the proposed scratch detection and repair method.