Celebratio Mathematica

R. Yin, D. Dunson, B. Cornelis, B. Brown, N. Ocon, and I. Daubechies: “Digital cradle removal in X-ray images of art paintings,” pp. 4299–4303 in 2014 IEEE international conference on image processing (ICIP) (Paris, 27–30 October 2014). IEEE (Piscataway, NJ), 2014. incollection

Rujie (Rachel) Yin	Related
Bruno Cornelis	Related
David Brian Dunson	Related
Noelle Ocon	Related
William P. Brown	Related

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

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.

Rujie (Rachel) Yin	Related
Bruno Cornelis	Related
Gábor Fodor	Related
David Brian Dunson	Related
Noelle Ocon	Related

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

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.

Bruno Cornelis	Related
Jianfeng Lu	Related
Haizhao Yang	Related
Noelle Ocon	Related
Alex Goodfriend	Related

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Ingrid Daubechies

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