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  • SPS
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    Length: 13:21
04 May 2020

Macro X-Ray Fluorescence (XRF) scanning is an increasingly widely used imaging technique for the non-invasive detection and mapping of chemical elements in Old Master paintings. Existing approaches for XRF signal analysis require varying degrees of expert user input. They are mainly based on peak fitting at fixed energies associated with each element and require the target elements to be selected manually. In this paper, we propose a new method that can process macro XRF scanning data from paintings fully automatically. The method consists of two parts: 1) detecting pulses in an XRF spectrum using Finite Rate of Innovation (FRI) theory; 2) producing the distribution maps for each element automatically identified in the painting. The results presented show the ability of our method to detect weak or partially overlapping signals and more excitingly to have visualisation of underdrawing in a masterpiece by Leonardo da Vinci.

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