<p>Document image restoration is a critical step in the preprocessing and analysis of digital images, aimed at recovering degraded document images. In recent years, generative models have achieved satisfactory results in the field of image restoration. However, the restoration results based on generative adversarial network methods exhibit issues such as color deviations, insufficient removal of overlapping regions, and unstable image quality. To address aforementioned issues, this paper proposes a document image restoration method based on diffusion models, specifically targeting the problem of restoring seen-through document images. This method follows a two-stage approach where the coarse stage focuses on restoring the primary contours of the foreground content, while the refinement stage handles detailed information. By introducing deep residual attention blocks and context-aware modules as two branches of the intermediate module, it is possible to better restore image details and enhance clarity. The proposed model combines pixel loss and diffusion loss to facilitate end-to-end training. Experimental results on the S-color0.5 dataset, DIBCO dataset, and MTDB dataset validate the effectiveness of the proposed method in improving color deviation issues and further enhancing the removal of overlapping regions seen-through.</p>