Počet záznamů: 1
Convolutional neural network exploiting pixel surroundings to reveal hidden features in artwork NIR reflectograms
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SYSNO ASEP 0562826 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Convolutional neural network exploiting pixel surroundings to reveal hidden features in artwork NIR reflectograms Tvůrce(i) Karella, Tomáš (UTIA-B) ORCID
Blažek, Jan (UTIA-B) RID, ORCID
Striová, J. (IT)Celkový počet autorů 3 Zdroj.dok. Journal of Cultural Heritage. - : Elsevier - ISSN 1296-2074
Roč. 58, November–December (2022), s. 186-198Poč.str. 13 s. Forma vydání Tištěná - P Jazyk dok. eng - angličtina Země vyd. NL - Nizozemsko Klíč. slova Signal separation ; Concealed features visualization ; Artwork analysis ; Infrared reflectography ; Convolutional neural networks Vědní obor RIV JC - Počítačový hardware a software Obor OECD Computer hardware and architecture CEP GA21-03921S GA ČR - Grantová agentura ČR Způsob publikování Omezený přístup Institucionální podpora UTIA-B - RVO:67985556 UT WOS 000877579300006 EID SCOPUS 85140310447 DOI 10.1016/j.culher.2022.09.022 Anotace 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.Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2023 Elektronická adresa https://www.sciencedirect.com/science/article/pii/S1296207422001637?via%3Dihub
Počet záznamů: 1