Celebratio Mathematica — Daubechies

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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

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

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.

Rujie (Rachel) Yin	Related
Bruno Cornelis	Related
Gábor Fodor	Related
Ann Dooms	Related