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Coupled dictionary learning for multimodal image super-resolution

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

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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.

Type: Proceedings paper
Title: Coupled dictionary learning for multimodal image super-resolution
Event: IEEE Global Conference on Signal and Information Processing (GlobalSIP), 7-9 December 2016, Washington, DC, USA
Location: Greater Washington D.C.
Dates: 07 December 2016
ISBN-13: 9781509045464
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/GlobalSIP.2016.7905824
Publisher version: http://dx.doi.org/10.1109/GlobalSIP.2016.7905824
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
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/1529221
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