Improvement of the visibility of concealed features in artwork NIR reflectograms by information separation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00468355" target="_blank" >RIV/67985556:_____/17:00468355 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.dsp.2016.09.007" target="_blank" >http://dx.doi.org/10.1016/j.dsp.2016.09.007</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.dsp.2016.09.007" target="_blank" >10.1016/j.dsp.2016.09.007</a>

Result language
angličtina
Original language name
Improvement of the visibility of concealed features in artwork NIR reflectograms by information separation
Original language description
Near Infrared (NIR) reflectography, coupled to visible (VIS) one, is a spectrophotometric imaging technique employed to probe both the inner and the outer layers of artworks. NIR reflectograms may partially contain information pertinent to the visible spectrum (due to the poor pigment transparency in NIR) and this decreases their comprehensibility. This work presents an innovative digital processing methodology for accentuating information contained in the infrared reflectograms. The proposed method consists of inducing minor changes in pixel intensity by suppressing VIS information content from NIR information content. The method creates such enhanced NIR reflectogram by extrapolating VIS reflectogram to a reflectogram recorded in NIR range and by subtracting it from the measured values in the near infrared spectral sub-band. As an extrapolator we suggest a feed forward artificial neural network (ANN). Significant results of improved visualization are exemplified on reflectograms acquired with a VIS-NIR 〈400,2250〉 nm〈400,2250〉 nm scanning device on real paintings such as Madonna dei Fusi attributed to Leonardo da Vinci. Parameters of the method, artificial neural network and separability of used pigments are discussed.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GAP103%2F12%2F2211" target="_blank" >GAP103/12/2211: Methods for multimodal image processing based on mathematical models of optical properties of paint materials for artwork analysis</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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