Information Technology: Faculty Publications

Reviving Fine Details in Historical Photographs Using A-ESRGAN: An AI Framework for Digital Heritage

Document Type

Conference Proceeding

Publication Date

12-31-2026

Publication Title

Proceedings of the 2025 IEEE 12th International Conference on Intelligent Computing and Information Systems, ICICIS 2025

DOI

10.1109/ICICIS66182.2025.11313137

Abstract

Restoring degraded historical photographs poses challenges, including loss of fine-grained detail, limited model transparency, and inadequate evaluation frameworks. This study presents an enhanced deep learning pipeline, Attention- Enhanced Super-Resolution Generative Adversarial Networks (A-ESRGAN), to address these gaps. By integrating attention mechanisms into the RRDBNet generator within the Real-ESRGAN framework, the model focuses on semantically meaningful regions, such as facial features, inscriptions, and textures, thereby improving perceptual and structural fidelity. We synthetically degraded the curated public-domain dataset with Gaussian blur and JPEG compression to mimic real-world conditions. We evaluated the model on paired low- and high-resolution images using PSNR and frequency-spectrum analysis. Results show that A-ESRGAN enhances PSNR and restores high-frequency details critical for visual clarity and historical interpretation. This research offers a scalable, interpretable framework for ethical and transparent AI-driven digital heritage restoration.

Comments

Georgia Southern University faculty member, Ataf Shalan co-authored, "Reviving Fine Details in Historical Photographs Using A-ESRGAN: An AI Framework for Digital Heritage."

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