Celebratio Mathematica — Daubechies

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N. Deligiannis, J. F. C. Mota, B. Cornelis, M. R. D. Rodrigues, and I. Daubechies: “X-ray image separation via coupled dictionary learning,” pp. 3533–3537 in 2016 IEEE international conference on image processing (Phoenix, AZ, 25–28 September 2016). IEEE (Piscataway, NJ), 2016. ArXiv 1605.06474 incollection

Miguel R. D. Rodrigues	Related
Bruno Cornelis	Related
João F. C. Mota	Related
Nikolaos Deligiannis	Related

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

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.

Miguel R. D. Rodrigues	Related
Bruno Cornelis	Related
João F. C. Mota	Related
Nikolaos Deligiannis	Related