Generative AI for digital color restoration in archaeology

Reconstructing lost pigments of the Harappan Civilization

Bibliographic Data

ID19487126
AuthorsMuhammad Nouman Akhtar (0000-0002-3367-1607, University of Genoa, corresponding author), Saverio Iacono (0000-0003-3002-6490, University of Genoa), Gianni Viardo Vercelli, Gianni Vercelli (University of Genoa), Hina Zulifiqar (Government Sadiq College Women University), Muhammad Suffian (0000-0002-1946-285X, Islamia University of Bahawalpur)
Year2026
Volume41
Pagese00549
Publication date2026-06-01
Open AccessYes
TypeARTICLE
VenueDigital Applications in Archaeology and Cultural Heritage (JOURNAL)
Journal identifiersISSN: 2212-0548
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.daach.2026.e00549
OpenAlexW7161246990
LanguageEN
References cited18

Color plays a crucial role in understanding the materiality, symbolism, and aesthetic identity of ancient artifacts. However, centuries of degradation have caused irreversible pigment loss in archaeological collections, limiting both scholarly interpretation and public appreciation. This study introduces a novel framework that employs Generative Artificial Intelligence (AI) to digitally reconstruct lost pigments in Harappan artifacts, one of the earliest urban cultures of South Asia. Using high-resolution images of terracotta pottery, seals, and figurines from museum and open-access archives, a diffusion-based generative Model was fine-tuned to predict original chromatic values by referencing extant pigment datasets and contextual archaeological metadata. The reconstructed images were then processed through a colorimetric calibration pipeline to ensure perceptual accuracy and consistency across devices. Quantitative evaluations compared generated hues with known pigment spectra, while expert validation from archaeologists assessed visual plausibility. The results demonstrate that generative AI can effectively simulate the original appearance of faded or eroded artifacts, offering new possibilities for virtual restoration, digital storytelling, and educational visualization. Beyond color recovery, the study highlights how AI-driven chromatic reconstruction contributes to heritage preservation and interpretation, providing a reproducible methodology for reimagining ancient aesthetics through computational means. This approach expands the frontier of digital color conservation by bridging heritage science, machine learning, and archaeological visualization within a sustainable, culturally sensitive framework

Civilization · Color analysis · Generative grammar · Image restoration · Industrial civilization · Aesthetic Perception and Analysis · Cultural Heritage Materials Analysis · Image Processing and 3D Reconstruction

Tools

Open DOIOpen Access