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Convolutional neural network exploiting pixel surroundings to reveal hidden features in artwork NIR reflectograms
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SYSNO ASEP 0562826 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Convolutional neural network exploiting pixel surroundings to reveal hidden features in artwork NIR reflectograms Author(s) Karella, Tomáš (UTIA-B) ORCID
Blažek, Jan (UTIA-B) RID, ORCID
Striová, J. (IT)Number of authors 3 Source Title Journal of Cultural Heritage. - : Elsevier - ISSN 1296-2074
Roč. 58, November–December (2022), s. 186-198Number of pages 13 s. Publication form Print - P Language eng - English Country NL - Netherlands Keywords Signal separation ; Concealed features visualization ; Artwork analysis ; Infrared reflectography ; Convolutional neural networks Subject RIV JC - Computer Hardware ; Software OECD category Computer hardware and architecture R&D Projects GA21-03921S GA ČR - Czech Science Foundation (CSF) Method of publishing Limited access Institutional support UTIA-B - RVO:67985556 UT WOS 000877579300006 EID SCOPUS 85140310447 DOI 10.1016/j.culher.2022.09.022 Annotation Near-infrared reflectography (NIR) is a well-established non-invasive and non-contact imaging technique. The NIR methods are able to reveal concealed layers of artwork, such as a painter’s sketch or repainted canvas. The information obtained may be helpful to historians for studying artist technique, attributing an artwork reconstructing faded details. Our research presents the improved method previously developed that reveals the hidden features by removing the information content of the visible spectrum from
NIR. Based on convolutional neural networks (CNN), our model estimates the transfer function from visible spectra to NIR, which is nonlinear and specific for painting materials. Its parameters are learnt for particular paintings on the subsamples randomly selected across the canvas, and the model is further utilised to enhance the whole artwork. In addition to the previously developed model, our algorithm exploits each pixel’s surroundings to estimate its NIR response. This leads to more precise results and increased robustness to various noises. We demonstrate higher accuracy than the previous method on the historical paintings mock-ups and higher performance on well-known artworks such as Madonna dei Fusi attributed to Leonardo da Vinci.Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2023 Electronic address https://www.sciencedirect.com/science/article/pii/S1296207422001637?via%3Dihub
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