Hyperparameter Optimisation of Artificial Intelligence for Digital REStoration of Cultural Heritages (AIRES-CH) Models

Artificial Intelligence for digital REStoration of Cultural Heritage (AIRES-CH) aims at building a web-based app for the digital restoration of pictorial artworks through Computer Vision technologies applied to physical imaging raw data. In previous work [7], it was shown that it is possible to develop a multidimensional deep neural network capable of inferring the RGB image from an X-Ray Fluorescence raw data. The developed network comprises two branches: a one-dimensional branch, which works pixel-by-pixel, and a two-dimensional branch, capable of performing image segmentation. In this project, we report the results of the hyperparameter optimisation of both branches.