Macro X-ray Fluorescence (MA-XRF) scanning is increasingly widely used by
researchers in heritage science to analyse easel paintings as one of a suite of
non-invasive imaging techniques. The task of processing the resulting MA-XRF
datacube generated in order to produce individual chemical element maps is
called MA-XRF deconvolution. While there are several existing methods that have
been proposed for MA-XRF deconvolution, they require a degree of manual
intervention from the user that can affect the final results. The
state-of-the-art AFRID approach can automatically deconvolute the datacube
without user input, but it has a long processing time and does not exploit
spatial dependency. In this paper, we propose two versions of a fast automatic
deconvolution (FAD) method for MA-XRF datacubes collected from easel paintings
with ADMM (alternating direction method of multipliers) and FISTA (fast
iterative shrinkage-thresholding algorithm). The proposed FAD method not only
automatically analyses the datacube and produces element distribution maps of
high-quality with spatial dependency considered, but also significantly reduces
the running time. The results generated on the MA-XRF datacubes collected from
two easel paintings from the National Gallery, London, verify the performance
of the proposed FAD method.Comment: 13 pages, 13 figure
