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

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.

Datacube segmentation via Deep Spectral Clustering

Extended Vision techniques are ubiquitous in physics. However, the data cubes steaming from such analysis often pose a challenge in their interpretation, due to the intrinsic difficulty in discerning the relevant information from the spectra composing the data cube. Furthermore, the huge dimensionality of data cube spectra poses a complex task in its statistical interpretation; nevertheless, this complexity contains a massive amount of statistical information that can be exploited in an unsupervised manner to outline some essential properties of the case study at hand, e.g.~it is possible to obtain an image segmentation via (deep) clustering of data-cube's spectra, performed in a suitably defined low-dimensional embedding space. To tackle this topic, we explore the possibility of applying unsupervised clustering methods in encoded space, i.e. perform deep clustering on the spectral properties of datacube pixels. A statistical dimensional reduction is performed by an ad hoc trained (Variational) AutoEncoder, in charge of mapping spectra into lower dimensional metric spaces, while the clustering process is performed by a (learnable) iterative K-Means clustering algorithm. We apply this technique to two different use cases, of different physical origins: a set of Macro mapping X-Ray Fluorescence (MA-XRF) synthetic data on pictorial artworks, and a dataset of simulated astrophysical observations.