Towards virtual painting recolouring using Vision Transformer on X-Ray Fluorescence datacubes

Autor:	Bombini, Alessandro, Bofías, Fernando García-Avello, Giambi, Francesca, Ruberto, Chiara
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Physics - Applied Physics I.4.m J.2
Druh dokumentu:	Working Paper
Popis:	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. Comment: v1: 20 pages, 10 figures; link to code repository
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.08826 Zobrazit plný text záznamu View this record from Arxiv