Digital Image Processing of The Ghent Altarpiece: Supporting the painting's study and conservation treatment

Pižurica, Aleksandra; Platiša, Ljiljana; Ružić, Tijana; Cornelis, Bruno; Dooms, Ann; Martens, Maximiliaan; Dubois, Hèléne; Devolder, Bart; Mey, Marc De; Daubechies, Ingrid

doi:10.1109/msp.2015.2411753

Cited by 30 publications

(31 citation statements)

References 27 publications

(57 reference statements)

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“…A crucial ingredient for a good performance of the non-local algorithm is its initialisation. In particular, once the inpainting domain is known, a pre-processing step where a local inpainting model, such as the TV inpainting model (5) with (6), can be run to provide a rough, but reliable initialisation of the algorithm [3] .…”

Section: Exemplar-based Inpaintingmentioning

confidence: 99%

Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts

et al. 2018

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The last 50 years have seen an impressive development of mathematical methods for the analysis and processing of digital images, mostly in the context of photography, biomedical imaging and various forms of engineering. The arts have been mostly overlooked in this process, apart from a few exceptional works in the last 10 years. With the rapid emergence of digitisation in the arts, however, the arts domain is becoming increasingly receptive to digital image processing methods and the importance of paying attention to this therefore increases. In this paper we discuss a range of mathematical methods for digital image restoration and digital visualisation for illuminated manuscripts. The latter provide an interesting opportunity for digital manipulation because they traditionally remain physically untouched. At the same time they also serve as an example for the possibilities mathematics and digital restoration offer as a generic and objective toolkit for the arts. Electronic supplementary material The online version of this article (10.1186/s40494-018-0216-z) contains supplementary material, which is available to authorized users.

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Section: Exemplar-based Inpaintingmentioning

confidence: 99%

Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts

et al. 2018

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“…We consider eight image pairs-each consisting of an X-ray scan and the corresponding photograph-taken from digital acquisitions [12] of single-sided panels of the Ghent Altarpiece (1432). Furthermore, we are given access to eight crack masks (one per visual/X-ray image pair) that indicate the pixel positions referring to cracks (these masks were obtained using our method in [10]).…”

Section: B Experiments With Real Datamentioning

confidence: 99%

“…In particular, the analysis of high-resolution multi-modal digital acquisitions of paintings in support of art scholarship has proved a challenging field of research. Examples include the numerical characterization of brushstrokes [5], [6] for the authentication or dating of paintings, canvas thread counting [7]- [9] with applications in art forensics, and the (semi-) automatic detection and digital inpainting of cracks [10]- [12].…”

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confidence: 99%

“…Currently, the Ghent Altarpiece is undergoing a major conservation and restoration campaign that is planned to end in 2017. The panels of the masterpiece were documented with various imaging modalities, amongst which visual macrophotography, infrared macrophotography, infrared reflectography and X-radiography [12]. A massive visual data set (comprising over 2TB of data) has been compiled by capturing small areas of the polyptych separately and stitching the resulting image blocks into one image per panel [13].…”

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confidence: 99%

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Multi-Modal Dictionary Learning for Image Separation With Application in Art Investigation

Deligiannis

Mota

Cornelis

et al. 2017

IEEE Trans. on Image Process.

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Abstract-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 frontand 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 models 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.

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“…After extensive experimental evaluation we found that for images of real-life paintings, the best performing two dictionaries are the discrete cosine transform and the dual-tree complex wavelet transform; the latter is the same dictionary as in [11] for the painting content. The canvas image I txt can be sparsely represented in the first dictionary, and the paint image the algorithms presented in [8], [9], [10], [11], [12], designed to precisely remove components in I out such as the wooden stretcher and cracks.…”

Section: Related Work and State-of-the-artmentioning

confidence: 99%

Removal of Canvas Patterns in Digital Acquisitions of Paintings

Cornelis

Yang

Goodfriend

et al. 2017

IEEE Trans. on Image Process.

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Abstract-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.Digital removal of canvas 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-

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Digital Image Processing of The Ghent Altarpiece: Supporting the painting's study and conservation treatment

Cited by 30 publications

References 27 publications

Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts

Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts

Multi-Modal Dictionary Learning for Image Separation With Application in Art Investigation

Removal of Canvas Patterns in Digital Acquisitions of Paintings

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