Coupled dictionary learning for multimodal image super-resolution

Pingfan Song, João F. C. Mota, Nikos Deligiannis, Miguel Raul Dias Rodrigues

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
PublisherIEEE
Pages162-166
Number of pages5
ISBN (Electronic)9781509045457
DOIs
Publication statusPublished - 24 Apr 2017
Event2016 IEEE Global Conference on Signal and Information Processing - Washington, United States
Duration: 7 Dec 20169 Dec 2016

Conference

Conference2016 IEEE Global Conference on Signal and Information Processing
Abbreviated titleGlobalSIP 2016
CountryUnited States
CityWashington
Period7/12/169/12/16

Keywords

  • Coupled dictionary learning
  • Multimodal data
  • Multispectral image super-resolution
  • Sequential recursive optimization
  • Sparse representation

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Song, P., Mota, J. F. C., Deligiannis, N., & Rodrigues, M. R. D. (2017). Coupled dictionary learning for multimodal image super-resolution. In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 162-166). IEEE. https://doi.org/10.1109/GlobalSIP.2016.7905824