MESA: A Training-Free Multi-Exemplar Deep Framework for Restoring Ancient Inscription Textures

TL;DR

MESA is a training-free, multi-exemplar deep learning framework that restores damaged ancient inscription textures by leveraging similar exemplar images and style-aware features.

Contribution

It introduces a novel exemplar-guided restoration method that encodes style and stroke features using Gram matrices and selects exemplars based on minimal displacement, addressing limitations of prior approaches.

Findings

MESA outperforms existing methods in restoring damaged inscriptions.

Layer-wise weighting improves the accuracy of texture and stroke reconstruction.

The method effectively guides restoration using exemplar images with minimal training.

Abstract

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.