MESA: A Training-Free Multi-Exemplar Deep Framework for Restoring Ancient Inscription Textures
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 (M...
| Authors | Vasileios Toulatzis, Sofia Theodoridou, Ioannis Fudos |
|---|---|
| Primary category | cs.CV |
| Published | 19 April 2026 |
| Last updated at source | 21 April 2026 |
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