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

Deligiannis, N; Mota, JFC; Cornelis, B; Rodrigues, MRD; Daubechies, I; (2016) Multi-Modal Dictionary Learning for Image Separation With Application In Art Investigation. IEEE Transactions on Image Processing , PP (99) p. 1. 10.1109/TIP.2016.2623484. Green open access

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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 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.

Type: Article
Title: Multi-Modal Dictionary Learning for Image Separation With Application In Art Investigation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TIP.2016.2623484
Publisher version: http://dx.doi.org/10.1109/TIP.2016.2623484
Language: English
Additional information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Art; Dictionaries; Imaging; Painting; Source separation; Visualization; X-ray imaging; Source separation; coupled dictionary learning; multi-modal data analysis; multi-scale image decomposition
UCL classification: UCL
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UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1529220
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