eprintid: 1529220 rev_number: 22 eprint_status: archive userid: 608 dir: disk0/01/52/92/20 datestamp: 2016-11-22 15:16:22 lastmod: 2021-10-04 00:08:50 status_changed: 2016-11-22 15:16:22 type: article metadata_visibility: show creators_name: Deligiannis, N creators_name: Mota, JFC creators_name: Cornelis, B creators_name: Rodrigues, MRD creators_name: Daubechies, I title: Multi-Modal Dictionary Learning for Image Separation With Application In Art Investigation ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F46 keywords: Art; Dictionaries; Imaging; Painting; Source separation; Visualization; X-ray imaging; Source separation; coupled dictionary learning; multi-modal data analysis; multi-scale image decomposition note: © 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. 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. date: 2016-10-31 date_type: published official_url: http://dx.doi.org/10.1109/TIP.2016.2623484 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1192405 doi: 10.1109/TIP.2016.2623484 lyricists_name: De Castro Mota, Joao lyricists_name: Rodrigues, Miguel lyricists_id: JFDEC19 lyricists_id: MRDIA06 actors_name: De Castro Mota, Joao actors_id: JFDEC19 actors_role: owner full_text_status: public publication: IEEE Transactions on Image Processing volume: PP number: 99 pagerange: 1-1 issn: 1941-0042 citation: 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 <https://doi.org/10.1109/TIP.2016.2623484>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/1529220/1/Final-IEEETIP2016.pdf