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      Global mapping of stratigraphy of an old-master painting using sparsity-based terahertz reflectometry

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          Abstract

          The process by which art paintings are produced typically involves the successive applications of preparatory and paint layers to a canvas or other support; however, there is an absence of nondestructive modalities to provide a global mapping of the stratigraphy, information that is crucial for evaluation of its authenticity and attribution, for insights into historical or artist-specific techniques, as well as for conservation. We demonstrate sparsity-based terahertz reflectometry can be applied to extract a detailed 3D mapping of the layer structure of the 17th century easel painting Madonna in Preghiera by the workshop of Giovanni Battista Salvi da Sassoferrato, in which the structure of the canvas support, the ground, imprimatura, underpainting, pictorial, and varnish layers are identified quantitatively. In addition, a hitherto unidentified restoration of the varnish has been found. Our approach unlocks the full promise of terahertz reflectometry to provide a global and detailed account of an easel painting’s stratigraphy by exploiting the sparse deconvolution, without which terahertz reflectometry in the past has only provided a meager tool for the characterization of paintings with paint-layer thicknesses smaller than 50  μm. The proposed modality can also be employed across a broad range of applications in nondestructive testing and biomedical imaging.

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          Most cited references29

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          An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

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            Terahertz spectroscopy and imaging - Modern techniques and applications

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              Terahertz time-gated spectral imaging for content extraction through layered structures

              Spatial resolution, spectral contrast and occlusion are three major bottlenecks for non-invasive inspection of complex samples with current imaging technologies. We exploit the sub-picosecond time resolution along with spectral resolution provided by terahertz time-domain spectroscopy to computationally extract occluding content from layers whose thicknesses are wavelength comparable. The method uses the statistics of the reflected terahertz electric field at subwavelength gaps to lock into each layer position and then uses a time-gated spectral kurtosis to tune to highest spectral contrast of the content on that specific layer. To demonstrate, occluding textual content was successfully extracted from a packed stack of paper pages down to nine pages without human supervision. The method provides over an order of magnitude enhancement in the signal contrast and can impact inspection of structural defects in wooden objects, plastic components, composites, drugs and especially cultural artefacts with subwavelength or wavelength comparable layers.
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                Author and article information

                Contributors
                junliang.dong@gatech.edu
                david.citrin@ece.gatech.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                8 November 2017
                8 November 2017
                2017
                : 7
                : 15098
                Affiliations
                [1 ]ISNI 0000 0001 2097 4943, GRID grid.213917.f, School of Electrical and Computer Engineering, , Georgia Institute of Technology, ; Atlanta, GA 30332-0250 USA
                [2 ]ISNI 0000 0001 0425 9771, GRID grid.463863.c, UMI 2958 Georgia Tech-CNRS, , Georgia Tech Lorraine, ; 2 Rue Marconi, Metz, 57070 France
                [3 ]Profilocolore srl, Via Spluga, Roma, 22 00 141 Italy
                Author information
                http://orcid.org/0000-0003-2215-912X
                Article
                15069
                10.1038/s41598-017-15069-2
                5678175
                29118333
                4d3ad558-1e87-404c-918e-ca3fffc03f7e
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 July 2017
                : 9 October 2017
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