Historical rubbing images often suffer from stroke breakage, material loss, uneven background interference, and age-related degradation, which make broken-stroke restoration in masked regions difficult. This challenge is particularly severe for oracle bone rubbings, where sparse strokes and damaged radicals require both structural continuity and local texture realism. To address this problem, we propose a three-stage generative adversarial image inpainting framework and evaluate it on oracle bone rubbing images as a focused case study. Stage I employs an LBP-guided coarse completion network to recover local binary texture priors in missing regions. Stage II introduces spatial-attention refinement and a dual-discriminator strategy to improve stroke continuity and local realism. Stage III uses a Swin-based refinement network to model long-range dependencies and enhance global consistency. A composite optimization objective combining reconstruction, weighted hole, perceptual, style, total-variation, and adversarial terms is used to coordinate the three stages. Experiments on oracle bone rubbing images with masking ratios from 10% to 40% show that the proposed framework produces visually coherent restorations and competitive quantitative results, reaching up to 35.18 dB PSNR and 0.9906 SSIM under the 10–20% masking setting. Because oracle bone glyph morphology is highly specialized, the current validation is intentionally restricted to this domain rather than overstating cross-domain generalization. The results show that the proposed framework can support digital conservation and recognition-oriented analysis of damaged oracle bone rubbing images.
Shen et al. (Tue,) studied this question.