bibtype C - Conference Paper (international conference)
ARLID 0490667
utime 20240103220150.5
mtime 20180626235959.9
SCOPUS 85049366167
WOS 000452025100058
DOI 10.1088/1757-899X/364/1/012058
title (primary) (eng) Improvement of the visibility of concealed features in misregistered NIR reflectograms by deep learning
specification
page_count 8 s.
media_type E
serial
ARLID cav_un_epca*0490666
ISSN 1757-8981
title Florence Heri-Tech - The Future of Heritage Science and Technologies
publisher
place Philadelphia
name IOP Science
year 2018
keyword NIR reflectograms
keyword VIS data
keyword DSLR cameras
author (primary)
ARLID cav_un_auth*0254045
name1 Blažek
name2 Jan
institution UTIA-B
full_dept (cz) Zpracování obrazové informace
full_dept (eng) Department of Image Processing
department (cz) ZOI
department (eng) ZOI
full_dept Department of Image Processing
country CZ
garant K
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0361943
name1 Vlašic
name2 O.
country CZ
author
ARLID cav_un_auth*0101238
name1 Zitová
name2 Barbara
institution UTIA-B
full_dept (cz) Zpracování obrazové informace
full_dept Department of Image Processing
department (cz) ZOI
department ZOI
full_dept Department of Image Processing
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2018/ZOI/blazek-0490667.pdf
cas_special
project
ARLID cav_un_auth*0279441
project_id LM2010005
agency GA MŠk
country CZ
project
ARLID cav_un_auth*0361425
project_id GA18-05360S
agency GA ČR
abstract (eng) Features of Old Master paintings hidden under the upper layer of a painting are often studied using NIR reflectograms, however their interpretability can be reduced due to the visible content. In our previous work [3] we described the possibility of increasing the visibility of concealed features in NIR reflectograms from the painting surface. The method output, enhanced NIR reflectogram, is produced by extrapolating the VIS data to a NIR range reflectogram and subtracting it from the acquired data in the NIR spectral subband. As a result, separated information from the NIR domain is obtiained. This method has a severe limitation, because it requires precise image registration of the VIS and NIR spectral bands. This is often hard to achieve, because DSLR cameras or multiple devices with various optical systems are used for data collection, and the mutual spatial relation of the images is often unknown. Thus, in the original form ,the algorithm was applicable only for data acquired using special scanners producing spatially registered images (as in [4]). In this work, we present an extension of the previous algorithm inspired by deep learning. The new concept allows processing of images only partially registered with pixel precision, subpixel accuracy is no longer needed. We suggest an extension of neural network input with neighboring pixels and allocation of extra ANN layers for translation compensation. The results are demonstrated on misregistered images captured by DSLR camera in VIS and NIR.
action
ARLID cav_un_auth*0361944
name Florence Heri-Tech - The Future of Heritage Science and Technologies
dates 20180516
mrcbC20-s 20180518
place Florence
country IT
RIV IN
FORD0 10000
FORD1 10200
FORD2 10201
reportyear 2019
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0284818
mrcbC61 1
cooperation
ARLID cav_un_auth*0325763
name Czech Technical University in Prague,Faculty of Nuclear Sciences and Physical Engineering
institution ČVUT FJFI
country CZ
confidential S
article_num 012058
mrcbC83 RIV/67985556:_____/18:00490667!RIV19-AV0-67985556 192095177 Doplnění UT WOS
mrcbC83 RIV/67985556:_____/18:00490667!RIV19-GA0-67985556 192084133 Doplnění UT WOS
mrcbC86 n.a. Proceedings Paper Archaeology|Architecture|Materials Science Multidisciplinary|Physics Applied|Imaging Science Photographic Technology
mrcbT16-s 0.197
mrcbT16-E Q4
arlyear 2018
mrcbU14 85049366167 SCOPUS
mrcbU24 PUBMED
mrcbU34 000452025100058 WOS
mrcbU63 cav_un_epca*0490666 Florence Heri-Tech - The Future of Heritage Science and Technologies 1757-8981 1757-899X Philadelphia IOP Science 2018 IOP Conference Series: Materials Science and Engineering Volume 364