When de Prony Met Leonardo: An Automatic Algorithm for Chemical Element Extraction From Macro X-Ray Fluorescence Data

Macro X-ray Fluorescence (MA-XRF) scanning is an increasingly widely used technique for analytical imaging of paintings and other artworks. The datasets acquired must be processed to produce maps showing the distribution chemical elements that are present in painting. Existing approaches require varying degrees expert user intervention, particular select a list target against which fit data. In this paper, we propose novel approach can automatically extract identify their distributions from MA-XRF datasets. proposed consists three parts: 1) pre-processing steps, 2) pulse detection model order selection based on Finite Rate Innovation theory, 3) element estimation Cramér-Rao bounding techniques. performance our assessed using collection National Gallery, London. results presented show ability detect with weak fluorescence intensity noisy XRF spectra, separate overlapping elemental signals and, excitingly, aid visualisation hidden underdrawing masterpiece by Leonardo da Vinci.