eprintid: 1529221 rev_number: 28 eprint_status: archive userid: 608 dir: disk0/01/52/92/21 datestamp: 2016-11-22 15:06:25 lastmod: 2021-10-05 22:41:09 status_changed: 2016-11-22 15:06:25 type: proceedings_section metadata_visibility: show creators_name: De Castro Mota, JF creators_name: Song, P creators_name: Deligiannis, N creators_name: Rodrigues, MRD title: Coupled dictionary learning for multimodal image super-resolution ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F46 keywords: coupled dictionary learning, multimodal data, sparse representation, sequential recursive optimization, multispectral image super-resolution, Dictionaries, Image resolution, Signal resolution, Data models, Training, Optimization, Sparse matrices note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: Real-world data processing problems often involve multiple data modalities, e.g., panchromatic and multispectral images, positron emission tomography (PET) and magnetic resonance imaging (MRI) images. As these modalities capture information associated with the same phenomenon, they must necessarily be correlated, although the precise relation is rarely known. In this paper, we propose a coupled dictionary learning (CDL) framework to automatically learn these relations. In particular, we propose a new data model to characterize both similarities and discrepancies between multimodal signals in terms of common and unique sparse representations with respect to a group of coupled dictionaries. However, learning these coupled dictionaries involves solving a highly non-convex structural dictionary learning problem. To address this problem, we design a coupled dictionary learning algorithm, referred to sequential recursive optimization (SRO) algorithm, to sequentially learn these dictionaries in a recursive manner. By capitalizing on our model and algorithm, we conceive a CDL based multimodal image super-resolution (SR) approach. Practical multispectral image SR experiments demonstrate that our SR approach outperforms the bicubic interpolation and the state-of-the-art dictionary learning based image SR approach, with Peak-SNR (PSNR) gains of up to 8.2 dB and 5.1 dB, respectively. date: 2017-04-24 date_type: published publisher: Institute of Electrical and Electronics Engineers (IEEE) official_url: http://dx.doi.org/10.1109/GlobalSIP.2016.7905824 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1192406 doi: 10.1109/GlobalSIP.2016.7905824 isbn_13: 9781509045464 lyricists_name: De Castro Mota, Joao lyricists_name: Rodrigues, Miguel lyricists_name: Song, Pingfan lyricists_id: JFDEC19 lyricists_id: MRDIA06 lyricists_id: PSONG06 actors_name: De Castro Mota, Joao actors_id: JFDEC19 actors_role: owner full_text_status: public series: Global Conference on Signal and Information Processing volume: 2016 place_of_pub: New York, USA pagerange: 162-166 event_title: IEEE Global Conference on Signal and Information Processing (GlobalSIP), 7-9 December 2016, Washington, DC, USA event_location: Greater Washington D.C. event_dates: 07 December 2016 institution: IEEE Global Conference on Signal and Information Processing (GlobalSIP) book_title: 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP) citation: De Castro Mota, JF; Song, P; Deligiannis, N; Rodrigues, MRD; (2017) Coupled dictionary learning for multimodal image super-resolution. In: 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). (pp. pp. 162-166). Institute of Electrical and Electronics Engineers (IEEE): New York, USA. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/1529221/1/CoupledDLforImgSR_FinalVer.pdf