先修复再思考：基于笔画引导的风险感知满文手稿修复
Think Before You Restore: Risk-Aware Manchu Manuscript Restoration with Stroke-Guided Attention
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中文总结 AI 辅助
针对满文手稿盲修复中的过度修复问题，提出SAGE-Restore框架，通过评估修复需求并利用笔画感知门控实现选择性修复，在恢复与保真间取得平衡。
中文摘要 AI 辅助
历史满文手稿的全页盲修复面临注释稀缺、退化区域未知和脆弱连笔等挑战。通用修复模型可能提升视觉质量，但常修改完好内容，导致过度修复。我们提出SAGE-Restore（笔画感知门控修复），一种选择性修复框架，首先评估需要修复的位置，然后利用该评估指导修复候选生成和像素级选择。其编码器从互补的外观和笔画结构线索预测补丁级修复概率，以调节修复候选生成，同时相应的修复逻辑被细化为像素级软门，选择性控制修复候选的应用位置。我们进一步引入保真度感知评估协议，联合衡量退化区域恢复、完好内容保留及其平衡。SAGE-Restore实现了最高的R-Recovery（0.463）和RFS（0.626），同时保持高U-Fidelity（0.968），展示了修复与内容保留之间的有效平衡。
英文摘要
Full-page blind restoration of historical Manchu manuscripts is challenging due to scarce annotations, unknown degradation regions, and fragile connected strokes. Generic restoration models may improve visual quality but often modify intact content, leading to over-restoration. We propose SAGE-Restore (Stroke-Aware Gated rEstoration), a selective restoration framework that first assesses where restoration is needed and then uses this assessment to guide restoration candidate generation and pixel-level selection. Its encoder predicts patch-level repair probabilities from complementary appearance and stroke-structural cues to condition restoration candidate generation, while the corresponding repair logits are refined into a pixel-level soft gate that selectively controls where the restoration candidate is applied. We further introduce a fidelity-aware evaluation protocol that jointly measures degraded-region recovery, intact-content preservation, and their balance. SAGE-Restore achieves the highest R-Recovery (0.463) and RFS (0.626), while maintaining high U-Fidelity (0.968), demonstrating an effective balance between restoration and content preservation.
发表机构
- Beijing Jiaotong University（北京交通大学）
- University of Florida（佛罗里达大学）
- Dalian University of Foreign Languages（大连外国语大学）
机构由 AI 辅助整理，请以论文原文为准。
