Ancient inscriptions frequently suffer missing or corrupted regions from fragmentation, erosion, or other damage, hindering reading, and analysis. We review prior image restoration methods and their applicability to inscription image recovery, then introduce MESA (Multi-Exemplar, Style-Aware) -an image-level restoration method that uses well-preserved exemplar inscriptions (from the same epigraphic monument, material, or similar letterforms) to guide reconstruction of damaged text. MESA encodes VGG19 convolutional features as Gram matrices to capture exemplar texture, style, and stroke structure; for each neural network layer it selects the exemplar minimizing Mean-Squared Displacement (MSD) to the damaged input. Layer-wise contribution weights are derived from Optical Character Recognition-estimated character widths in the exemplar set to bias filters toward scales matching letter geometry, and a training mask preserves intact regions so synthesis is restricted to damaged areas. We also summarize prior network architectures and exemplar and single-image synthesis, inpainting, and Generative Adversarial Network (GAN) approaches, highlighting limitations that MESA addresses. Comparative experiments demonstrate the advantages of MESA. Finally, we provide a practical roadmap for choosing restoration strategies given available exemplars and metadata.

翻译：古代碑文常因碎裂、侵蚀或其他损伤导致文字缺失或区域损坏，阻碍阅读与分析。本文回顾了现有图像修复方法及其在碑文图像恢复中的适用性，并提出MESA（多示例、风格感知）——一种利用保存完好的示例碑文（来自同一金石纪念碑、材质或相似字形）指导受损文字重建的图像级修复方法。MESA将VGG19卷积特征编码为格拉姆矩阵，捕捉示例纹理、风格与笔画结构；针对每个神经网络层，选取与受损输入均方位移最小的示例。层间贡献权重由示例集中光学字符识别估算的字符宽度推导得出，使滤波器偏向匹配文字几何的尺度，并通过训练掩码保留完整区域，将合成限定于受损区域。本文还总结了现有网络架构及示例与单图像合成、修复及生成对抗网络方法，突出MESA解决的局限性。对比实验展示了MESA的优势。最后，我们根据可用示例与元数据，提供了选择修复策略的实用路线图。