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.
@article{arxiv.2410.08826,
title = {Towards virtual painting recolouring using Vision Transformer on X-Ray Fluorescence datacubes},
author = {Alessandro Bombini and Fernando García-Avello Bofías and Francesca Giambi and Chiara Ruberto},
journal= {arXiv preprint arXiv:2410.08826},
year = {2024}
}