B. Cornelis, A. Dooms, I. Daubechies, and P. Schelkens :
Report on digital image processing for art historians 2009 .
In online proceedings “SAMPTA’09: SAMPling Theory and Applications,” L. Fesquet and B. Torrésani, eds. (Marseille, France, 18–22 May 2009).
misc
Abstract
People
BibTeX
As art museums are digitizing their collections, a crossdisciplinary interaction between image analysts, mathematicians and art historians is emerging, putting to use recent advances made in the field of image processing (in acquisition as well as in analysis). An example of this is the Digital Painting Analysis (DPA) initiative, bringing together several research teams from universities and museums to tackle art related questions such as artist authentication, dating, etc. Some of these questions were formulated by art historians as challenges for the research teams. The results, mostly on van Gogh paintings, were presented at two workshops. As part of the Princeton team within the DPA initiative we give an overview of the work that was performed so far.
@misc {key54588328,
AUTHOR = {Cornelis, B. and Dooms, A. and Daubechies,
I. and Schelkens, Peter},
TITLE = {Report on digital image processing for
art historians},
HOWPUBLISHED = {In online proceedings ``SAMPTA'09: SAMPling
Theory and Applications'', L. Fesquet
and B. Torr\'esani, eds. (Marseille,
France, 18--22 May 2009)},
YEAR = {2009},
PAGES = {189--192},
URL = {https://hal.archives-ouvertes.fr/hal-00452288/document},
}
L. Platiša, B. Cornelis, T. Ružić, A. Pižurica, A. Dooms, M. Martens, M. De Mey, and I. Daubechies :
“Spatiogram features to characterize pearls in paintings 801–804
in
18th IEEE international conference on image processing
(Brussels, 11–14 September 2011 ).
IEEE (Piscataway, NJ ),
2011 .
incollection
Abstract
People
BibTeX
Objective characterization of jewels in paintings, especially pearls, has been a long lasting challenge for art historians. The way an artist painted pearls reflects his ability to observing nature and his knowledge of contemporary optical theory. Moreover, the painterly execution may also be considered as an individual characteristic useful in distinguishing hands. In this work, we propose a set of image analysis techniques to analyze and measure spatial characteristics of the digital images of pearls, all relying on the so called spatiogram image representation. Our experimental results demonstrate good correlation between the new metrics and the visually observed image features, and also capture the degree of realism of the visual appearance in the painting. In that sense, these results set the basis in creating a practical tool for art historical attribution and give strong motivation for further investigations in this direction.
@incollection {key21554432,
AUTHOR = {Plati\v{s}a, L. and Cornelis, B. and
Ru\v{z}i\'c, T. and Pi\v{z}urica, A.
and Dooms, A. and Martens, M. and De
Mey, M. and Daubechies, I.},
TITLE = {Spatiogram features to characterize
pearls in paintings},
BOOKTITLE = {18th {IEEE} international conference
on image processing},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2011},
PAGES = {801--804},
DOI = {10.1109/ICIP.2011.6116677},
NOTE = {(Brussels, 11--14 September 2011).},
ISBN = {9781457713040},
}
T. Ružić, B. Cornelis, L. Platiša, A. Pižurica, A. Dooms, W. Philips, M. Martens, M. De Mey, and I. Daubechies :
“Virtual restoration of the Ghent Altarpiece using crack detection and inpainting 417–428
in
Advanced concepts for intelligent vision systems: 13th international conference
(Ghent, Belgium, 22–25 August 2011 ).
Edited by J. Blanc-Talon, R. Kleihorst, W. Philips, D. Popescu, and P. Scheunders .
Lecture Notes in Computer Science 6915 .
Springer (Berlin ),
2011 .
incollection
Abstract
People
BibTeX
In this paper, we present a new method for virtual restoration of digitized paintings, with the special focus on the Ghent Altarpiece (1432), one of Belgium’s greatest masterpieces. The goal of the work is to remove cracks from the digitized painting thereby approximating how the painting looked like before ageing for nearly 600 years and aiding art historical and palaeographical analysis. For crack detection, we employ a multiscale morphological approach, which can cope with greatly varying thickness of the cracks as well as with their varying intensities (from dark to the light ones). Due to the content of the painting (with extremely many fine details) and complex type of cracks (including inconsistent whitish clouds around them), the available inpainting methods do not provide satisfactory results on many parts of the painting. We show that patch-based methods outperform pixel-based ones, but leaving still much room for improvements in this application. We propose a new method for candidate patch selection, which can be combined with different patch-based inpainting methods to improve their performance in crack removal. The results demonstrate improved performance, with less artefacts and better preserved fine details.
@incollection {key41995092,
AUTHOR = {Ru\v{z}i\'c, Tijana and Cornelis, Bruno
and Plati\v{s}a, Ljiljana and Pi\v{z}urica,
Aleksandra and Dooms, Ann and Philips,
Wilfried and Martens, Maximiliaan and
De Mey, Marc and Daubechies, Ingrid},
TITLE = {Virtual restoration of the {G}hent {A}ltarpiece
using crack detection and inpainting},
BOOKTITLE = {Advanced concepts for intelligent vision
systems: 13th international conference},
EDITOR = {Blanc-Talon, Jacques and Kleihorst,
Richard and Philips, Wilfried and Popescu,
Dan and Scheunders, Paul},
SERIES = {Lecture Notes in Computer Science},
NUMBER = {6915},
PUBLISHER = {Springer},
ADDRESS = {Berlin},
YEAR = {2011},
PAGES = {417--428},
DOI = {10.1007/978-3-642-23687-7_38},
NOTE = {(Ghent, Belgium, 22--25 August 2011).},
ISSN = {0302-9743},
ISBN = {9783642236860},
}
B. Cornelis, Y. Yang, J. T. Vogelstein, A. Dooms, I. Daubechies, and D. Dunson :
“Bayesian crack detection in ultra high resolution multimodal images of paintings ,”
pp. 1–8
in
18th international conference on digital signal processing
(Fira, Greece, 1–3 July 2013 ).
IEEE (Piscataway, NJ ),
2013 .
ArXiv
1304.5894 incollection
Abstract
People
BibTeX
The preservation of our cultural heritage is of paramount importance. Thanks to recent developments in digital acquisition techniques, powerful image analysis algorithms are developed which can be useful non-invasive tools to assist in the restoration and preservation of art. In this paper we propose a semi-supervised crack detection method that can be used for high-dimensional acquisitions of paintings coming from different modalities. Our dataset consists of a recently acquired collection of images of The Ghent Altarpiece (1432), one of Northern Europe’s most important art masterpieces. Our goal is to build a classifier that is able to discern crack pixels from the background consisting of non-crack pixels, making optimal use of the information that is provided by each modality. To accomplish this we employ a recently developed non-parametric Bayesian classifier, that uses tensor factorizations to characterize any conditional probability. A prior is placed on the parameters of the factorization such that every possible interaction between predictors is allowed while still identifying a sparse subset among these predictors. The proposed Bayesian classifier, which we will refer to as conditional Bayesian tensor factorization or CBTF, is assessed by visually comparing classification results with the Random Forest (RF) algorithm.
@incollection {key1304.5894a,
AUTHOR = {Bruno Cornelis and Yun Yang and Joshua
T. Vogelstein and Ann Dooms and Ingrid
Daubechies and David Dunson},
TITLE = {Bayesian crack detection in ultra high
resolution multimodal images of paintings},
BOOKTITLE = {18th international conference on digital
signal processing},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2013},
PAGES = {1--8},
NOTE = {(Fira, Greece, 1--3 July 2013). ArXiv:1304.5894.},
ISBN = {9781467358071},
}
B. Cornelis, T. Ružić, E. Gezels, A. Dooms, A. Pižurica, L. Platiša, J. Cornelis, M. Martens, M. De Mey, and I. Daubechies :
“Crack detection and inpainting for virtual restoration of paintings: The case of the Ghent Altarpiece Signal Processing
93 : 3
(March 2013 ),
pp. 605–619 .
article
Abstract
People
BibTeX
Digital image processing is proving to be of great help in the analysis and documentation of our vast cultural heritage. In this paper, we present a new method for the virtual restoration of digitized paintings with special attention for The Ghent Altarpiece (1432), a large polyptych panel painting of which very few digital reproductions exist. We achieve our objective by detecting and digitally removing cracks. The detection of cracks is particularly difficult because of the varying content features in different parts of the polyptych. Three new detection methods are proposed and combined in order to detect cracks of different sizes as well as varying brightness. Semi-supervised clustering based post-processing is used to remove objects falsely labelled as cracks. For the subsequent inpainting stage, a patch-based technique is applied to handle the noisy nature of the images and to increase the performance for crack removal. We demonstrate the usefulness of our method by means of a case study where the goal is to improve readability of the depiction of text in a book, present in one of the panels, in order to assist paleographers in its deciphering.
@article {key33739184,
AUTHOR = {Cornelis, B. and Ru\v{z}i\'c, T. and
Gezels, E. and Dooms, A. and Pi\v{z}urica,
A. and Plati\v{s}a, L. and Cornelis,
J. and Martens, M. and De Mey, M. and
Daubechies, I.},
TITLE = {Crack detection and inpainting for virtual
restoration of paintings: {T}he case
of the {G}hent {A}ltarpiece},
JOURNAL = {Signal Processing},
FJOURNAL = {Signal Process.},
VOLUME = {93},
NUMBER = {3},
MONTH = {March},
YEAR = {2013},
PAGES = {605--619},
DOI = {10.1016/j.sigpro.2012.07.022},
ISSN = {0165-1684},
}
B. Cornelis, A. Dooms, I. Daubechies, and D. Dunson :
“Bayesian crack detection in high resolution data ,”
pp. 16–18
in
Proceedings of the second “international Traveling Workshop on Interactions between Sparse models and Technology” (iTWIST’14) .
2014 .
incollection
People
BibTeX
@incollection {key96114383,
AUTHOR = {Bruno Cornelis and Ann Dooms and Ingrid
Daubechies and David Dunson},
TITLE = {Bayesian crack detection in high resolution
data},
BOOKTITLE = {Proceedings of the second ``international
{T}raveling {W}orkshop on {I}nteractions
between {S}parse models and {T}echnology''
(i{TWIST}'14)},
YEAR = {2014},
PAGES = {16-18},
}
L. Jacques, C. De Vleeschouwer, Y. Boursier, P. Sudhakar, C. De Mol, A. Pizurica, S. Anthoine, P. Vandergheynst, P. Frossard, C. Bilen, S. Kitic, N. Bertin, R. Gribonval, N. Boumal, B. Mishra, P.-A. Absil, R. Sepulchre, S. Bundervoet, C. Schretter, A. Dooms, P. Schelkens, O. Chabiron, F. Malgouyres, J.-Y. Tourneret, P. Dobigeon, N. Chainais, C. Richard, B. Cornelis, I. Daubechies, D. Dunson, M. Dankova, P. Rajmic, K. Degraux, V. Cambareri, B. Geelen, G. Lafruit, G. Setti, J.-F. Determe, J. Louveaux, F. Horlin, A. Drémeau, P. Heas, C. Herzet, V. Duval, G. Peyré, A. Fawzi, M. Davies, N. Gillis, S. A. Vavasis, C. Soussen, L. Le Magoarou, J. Liang, J. Fadili, A. Liutkus, D. Martina, S. Gigan, L. Daudet, M. Maggioni, S. Minsker, N. Strawn, C. Mory, F. Ngole, J.-L. Starck, I. Loris, S. Vaiter, M. Golbabaee, and D. Vukobratovic :
Proceedings of the second “international Traveling Workshop on Interactions between Sparse models and Technology” (iTWIST’14) .
Preprint ,
October 2014 .
Namur, Belgium, 27–29 August 2014.
techreport
Abstract
People
BibTeX
The implicit objective of the biennial “international–Traveling Workshop on Interactions between Sparse models and Technology” (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. iTWIST’14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the “sparsity paradigm”: Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What’s next?; Sparse machine learning and inference.
@techreport {key89916570,
AUTHOR = {Jacques, L. and De Vleeschouwer, C.
and Boursier, Y. and Sudhakar, P. and
De Mol, C. and Pizurica, A. and Anthoine,
S. and Vandergheynst, P. and Frossard,
P. and Bilen, C. and Kitic, S. and Bertin,
N. and Gribonval, R. and Boumal, N.
and Mishra, B. and Absil, P.-A. and
Sepulchre, R. and Bundervoet, S. and
Schretter, C. and Dooms, A. and Schelkens,
P. and Chabiron, O. and Malgouyres,
F. and Tourneret, J.-Y. and Dobigeon,
P. and Chainais, N. and Richard, C.
and Cornelis, B. and Daubechies, I.
and Dunson, D. and Dankova, M. and Rajmic,
P. and Degraux, K. and Cambareri, V.
and Geelen, B. and Lafruit, G. and Setti,
G. and Determe, J.-F. and Louveaux,
J. and Horlin, F. and Dr\'emeau, A.
and Heas, P. and Herzet, C. and Duval,
V. and Peyr\'e, G. and Fawzi, A. and
Davies, M. and Gillis, N. and Vavasis,
S. A. and Soussen, C. and Le Magoarou,
L. and Liang, J. and Fadili, J. and
Liutkus, A. and Martina, D. and Gigan,
S. and Daudet, L. and Maggioni, M. and
Minsker, S. and Strawn, N. and Mory,
C. and Ngole, F. and Starck, J.-L. and
Loris, I. and Vaiter, S. and Golbabaee,
M. and Vukobratovic, D.},
TITLE = {Proceedings of the second ``international
{T}raveling {W}orkshop on {I}nteractions
between {S}parse models and {T}echnology''
(i{TWIST}'14)},
TYPE = {Preprint},
MONTH = {October},
YEAR = {2014},
PAGES = {69},
NOTE = {Namur, Belgium, 27--29 August 2014.},
}
R. Yin, D. Dunson, B. Cornelis, B. Brown, N. Ocon, and I. Daubechies :
“Digital cradle removal in X-ray images of art paintings 4299–4303
in
2014 IEEE international conference on image processing (ICIP)
(Paris, 27–30 October 2014 ).
IEEE (Piscataway, NJ ),
2014 .
incollection
Abstract
People
BibTeX
We introduce an algorithm that removes the deleterious effect of cradling on X-ray images of paintings on wooden panels. The algorithm consists of a three stage procedure. Firstly, the cradled regions are located automatically. The second step consists of separating the X-ray image into a textural and image component. In the last step the algorithm learns to distinguish between the texture caused by the wooden cradle and the texture belonging to the original painted wooden panel. The results obtained with our method are compared with those obtained manually by best current practice.
@incollection {key84927847,
AUTHOR = {Rujie Yin and David Dunson and Bruno
Cornelis and Bill Brown and Noelle Ocon
and Ingrid Daubechies},
TITLE = {Digital cradle removal in {X}-ray images
of art paintings},
BOOKTITLE = {2014 {IEEE} international conference
on image processing ({ICIP})},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2014},
PAGES = {4299--4303},
DOI = {10.1109/ICIP.2014.7025873},
NOTE = {(Paris, 27--30 October 2014).},
ISBN = {9781479957514},
}
A. Pižurica, L. Platiša, T. Ružić, B. Cornelis, A. Dooms, M. Martens, H. Dubois, B. Devolder, M. D. Mey, and I. Daubechies :
“Digital image processing of T he Ghent Altarpiece: Supporting the painting’s study and conservation treatment IEEE Signal Process. Mag.
32 : 4
(July 2015 ),
pp. 112–122 .
article
People
BibTeX
@article {key88873596,
AUTHOR = {Aleksandra Pi\v{z}urica and Ljiljana
Plati\v{s}a and Tijana Ru\v{z}i\'c and
Bruno Cornelis and Ann Dooms and Maximiliaan
Martens and H\'el\`ene Dubois and Bart
Devolder and Marc De Mey and Ingrid
Daubechies},
TITLE = {Digital image processing of \textit{{T}he
{G}hent {A}ltarpiece}: {S}upporting
the painting's study and conservation
treatment},
JOURNAL = {IEEE Signal Process. Mag.},
FJOURNAL = {IEEE Signal Processing Magazine},
VOLUME = {32},
NUMBER = {4},
MONTH = {July},
YEAR = {2015},
PAGES = {112--122},
DOI = {10.1109/MSP.2015.2411753},
ISSN = {1053-5888},
}
N. Deligiannis, J. F. C. Mota, B. Cornelis, M. R. D. Rodrigues, and I. Daubechies :
“X-ray image separation via coupled dictionary learning 3533–3537
in
2016 IEEE international conference on image processing
(Phoenix, AZ, 25–28 September 2016 ).
IEEE (Piscataway, NJ ),
2016 .
ArXiv
1605.06474 incollection
Abstract
People
BibTeX
In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. Unlike prior source separation methods, which are based on statistical or structural incoherence of the sources, we use visual images taken from the front- and back-side of the panel to drive the separation process. The coupling of the two imaging modalities is achieved via a new multi-scale dictionary learning method. Experimental results demonstrate that our method succeeds in the discrimination of the sources, while state-of-the-art methods fail to do so.
@incollection {key1605.06474a,
AUTHOR = {Deligiannis, Nikos and Mota, Jo\~{a}o
F. C. and Cornelis, Bruno and Rodrigues,
Miguel R. D. and Daubechies, Ingrid},
TITLE = {X-ray image separation via coupled dictionary
learning},
BOOKTITLE = {2016 {IEEE} international conference
on image processing},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2016},
PAGES = {3533--3537},
DOI = {10.1109/ICIP.2016.7533017},
NOTE = {(Phoenix, AZ, 25--28 September 2016).
ArXiv:1605.06474.},
ISSN = {2381-8549},
ISBN = { 9781467399616},
}
R. Yin, B. Cornelis, G. Fodor, N. Ocon, D. Dunson, and I. Daubechies :
“Removing cradle artifacts in X-ray images of paintings SIAM J. Imaging Sci.
9 : 3
(2016 ),
pp. 1247–1272 .
MR
3541996 Zbl
06665850 article
Abstract
People
BibTeX
We propose an algorithm that removes the visually unpleasant effects of cradling in X-ray images of panel paintings, with the goal of improving the X-ray image readability by art experts. The algorithm consists of three stages. In the first stage the location of the cradle is detected automatically and the grayscale inconsistency, caused by the thickness of the cradle, is corrected. In a second stage we use a method called morphological component analysis to separate the X-ray image into a so-called cartoon part and a texture part, where the latter contains mostly the wood grain from both the panel and the cradling. The algorithm next learns a Bayesian factor model that distinguishes between the texture patterns that originate from the cradle and those from other components such as the panel and/or the painting on the panel surface, and finally uses this to remove the textures associated with the cradle. We apply the algorithm to a number of historically important paintings on panel. We also show how it can be used to digitally remove stretcher artifacts from X-rays of paintings on canvas. We compare our results with those obtained manually by best current practices in art conservation as well as on a ground truth dataset, consisting of X-ray images of a painting before and after removal of the physically attached cradle.
@article {key3541996m,
AUTHOR = {Yin, Rujie and Cornelis, Bruno and Fodor,
Gabor and Ocon, Noelle and Dunson, David
and Daubechies, Ingrid},
TITLE = {Removing cradle artifacts in {X}-ray
images of paintings},
JOURNAL = {SIAM J. Imaging Sci.},
FJOURNAL = {SIAM Journal on Imaging Sciences},
VOLUME = {9},
NUMBER = {3},
YEAR = {2016},
PAGES = {1247--1272},
DOI = {10.1137/15M1053554},
NOTE = {MR:3541996. Zbl:06665850.},
ISSN = {1936-4954},
}
B. Cornelis, H. Yang, A. Goodfriend, N. Ocon, J. Lu, and I. Daubechies :
“Removal of canvas patterns in digital acquisitions of paintings IEEE Trans. Image Process.
26 : 1
(2017 ),
pp. 160–171 .
MR
3579347 article
Abstract
People
BibTeX
We address the removal of canvas artifacts from high-resolution digital photographs and X-ray images of paintings on canvas. Both imaging modalities are common investigative tools in art history and art conservation. Canvas artifacts manifest themselves very differently according to the acquisition modality; they can hamper the visual reading of the painting by art experts, for instance, in preparing a restoration campaign. Computer-aided canvas removal is desirable for restorers when the painting on canvas they are preparing to restore has acquired over the years a much more salient texture. We propose a new algorithm that combines a cartoon-texture decomposition method with adaptive multiscale thresholding in the frequency domain to isolate and suppress the canvas components. To illustrate the strength of the proposed method, we provide various examples, for acquisitions in both imaging modalities, for paintings with different types of canvas and from different periods. The proposed algorithm outperforms previous methods proposed for visual photographs such as morphological component analysis and Wiener filtering and it also works for the digital removal of canvas artifacts in X-ray images.
@article {key3579347m,
AUTHOR = {Cornelis, Bruno and Yang, Haizhao and
Goodfriend, Alex and Ocon, Noelle and
Lu, Jianfeng and Daubechies, Ingrid},
TITLE = {Removal of canvas patterns in digital
acquisitions of paintings},
JOURNAL = {IEEE Trans. Image Process.},
FJOURNAL = {IEEE Transactions on Image Processing},
VOLUME = {26},
NUMBER = {1},
YEAR = {2017},
PAGES = {160--171},
DOI = {10.1109/TIP.2016.2621413},
NOTE = {MR:3579347.},
ISSN = {1057-7149},
}
N. Deligiannis, J. F. C. Mota, B. Cornelis, M. R. D. Rodrigues, and I. Daubechies :
“Multi-modal dictionary learning for image separation with application in art investigation IEEE Trans. Image Process.
26 : 2
(2017 ),
pp. 751–764 .
MR
3604821 ArXiv
1607.04147 article
Abstract
People
BibTeX
In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front-and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component captures features shared by the multi-modal images, whereas the innovation component captures modality-specific information. As such, our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the X-rays. Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement. Moreover, to improve further on the visual quality of the separated images, we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure. Experimentation on synthetic and real data–taken from digital acquisition of the Ghent Altarpiece (1432)–confirms the superiority of our method against the state-of-the-art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation.
@article {key3604821m,
AUTHOR = {Deligiannis, Nikos and Mota, Jo\~ao
F. C. and Cornelis, Bruno and Rodrigues,
Miguel R. D. and Daubechies, Ingrid},
TITLE = {Multi-modal dictionary learning for
image separation with application in
art investigation},
JOURNAL = {IEEE Trans. Image Process.},
FJOURNAL = {IEEE Transactions on Image Processing},
VOLUME = {26},
NUMBER = {2},
YEAR = {2017},
PAGES = {751--764},
DOI = {10.1109/TIP.2016.2623484},
NOTE = {ArXiv:1607.04147. MR:3604821.},
ISSN = {1057-7149},
}
G. Fodor, B. Cornelis, R. Yin, A. Dooms, and I. Daubechies :
“Cradle removal in X-ray images of panel paintings IPOL J. Image Process. Online
7
(2017 ),
pp. 23–42 .
MR
3608123 article
Abstract
People
BibTeX
We address the problem of mitigating the visually displeasing effects of cradling in X-ray images of panel paintings. The proposed algorithm consists of three stages. In the first stage the location of the cradling is detected semi-automatically and the grayscale inconsistency, caused by the thickness of the cradling, is adjusted. In a second stage we use a blind source separation method to decompose the X-ray image into a so-called cartoon part and a texture part, where the latter contains mostly the wood grain from both the panel as well as the cradling. In the third and final stage the algorithm tries to learn the distinction between the texture patterns that originate from the cradling and those from other components such as the panel and/or the painting. The goal of the proposed research is to improve the readability of X-ray images of paintings for art experts.
@article {key3608123m,
AUTHOR = {Fodor, G\'abor and Cornelis, Bruno and
Yin, Rujie and Dooms, Ann and Daubechies,
Ingrid},
TITLE = {Cradle removal in {X}-ray images of
panel paintings},
JOURNAL = {IPOL J. Image Process. Online},
FJOURNAL = {IPOL Journal. Image Processing Online},
VOLUME = {7},
YEAR = {2017},
PAGES = {23--42},
DOI = {10.5201/ipol.2017.174},
NOTE = {MR:3608123.},
ISSN = {2105-1232},
}
