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：古代铭文常因碎裂、侵蚀或其他损伤而出现缺失或破损区域，阻碍阅读与分析。我们回顾了现有图像修复方法及其在铭文图像恢复中的适用性，随后提出MESA（多范例、风格感知）——一种图像级修复方法，利用保存完好的范例铭文（来自同一碑刻、材质或相似字形）指导受损文字的复原。MESA将VGG19卷积特征编码为格拉姆矩阵以捕获范例纹理、风格与笔画结构；针对每个神经网络层，选取使均方位移（MSD）最小的范例处理受损输入。通过光学字符识别估计范例集中字符宽度推导逐层贡献权重，使滤波器偏向匹配字母几何比例的尺度；训练掩码保留完整区域，将合成限制在受损范围内。我们还总结了现有网络架构及范例/单图像合成、修复和生成对抗网络（GAN）方法，指出MESA解决的关键局限。对比实验证明了MESA的优势。最后，我们根据可用范例与元数据提供选择修复策略的实用路线图。