Work

Towards virtual painting recolouring using vision transformer on x-ray fluorescence datacubes

Year: 2025

Type: article

Abstract: In this contribution, we define (and test) a pipeline to perform virtual painting recolouring using raw data of x-ray Fluorescence (XRF) analysis on pictorial artworks. To circumvent the small dataset size, we generate a synthetic dataset, starting from a database of XRF spectra; furthermore, to ensure a better generalisation capacity (and to tackle the issue of in-memory size and inference time), we define a Deep Variational Embedding network to embed the XRF spectra into a lower dimensional, K-Means friendly, metric space. We thus train a set of models to assign coloured images to embedded XRF images. We report here the devised pipeline performances in terms of visual quality metrics, and we close on a discussion on the results.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/adb937

Authors: Alessandro Bombini, Fernando García-Avello Bofías, Francesca Giambi, Chiara Ruberto

Institutions: Istituto Nazionale di Fisica Nucleare, Istituto Nazionale di Fisica Nucleare, Sezione di Firenze, Fondazione ICSC Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing, University of Florence

Language: English

Topic: Cultural Heritage Materials Analysis

Subfield: Archeology

Field: Arts and Humanities

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $3,000

Funder: H2020 Societal Challenges