A fast automatic method for deconvoluting macro X-ray fluorescence data collected from easel paintings
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
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.
Date Issued
2023-01-01
Date Acceptance
2023-05-27
Citation
IEEE Transactions on Computational Imaging, 2023, 9, pp.649-664
ISSN
2573-0436
Publisher
Institute of Electrical and Electronics Engineers
Start Page
649
End Page
664
Journal / Book Title
IEEE Transactions on Computational Imaging
Volume
9
Copyright Statement
Copyright © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects
ADMM
ALGORITHM
Engineering
Engineering, Electrical & Electronic
FEATURES
finite rate of innovation
FISTA
Imaging Science & Photographic Technology
Macro X-ray Fluorescence scanning
matrix factorisation
PIXE
Science & Technology
SPECTRUM
Technology
XRF deconvolution
Publication Status
Published
Date Publish Online
2023-06-21