TY  - GEN
PB  - Institute of Electrical and Electronics Engineers (IEEE)
UR  - http://dx.doi.org/10.1109/GlobalSIP.2016.7905824
N2  - 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.
ID  - discovery1529221
A1  - De Castro Mota, JF
A1  - Song, P
A1  - Deligiannis, N
A1  - Rodrigues, MRD
T3  - Global Conference on Signal and Information Processing
KW  - coupled dictionary learning
KW  -  multimodal data
KW  -  sparse representation
KW  -  sequential recursive optimization
KW  - 
multispectral image super-resolution
KW  -  Dictionaries
KW  - 
Image resolution
KW  -  Signal resolution
KW  -  Data models
KW  -  Training
KW  - 
Optimization
KW  -  Sparse matrices
CY  - New York, USA
SP  - 162
AV  - public
Y1  - 2017/04/24/
EP  - 166
TI  - Coupled dictionary learning for multimodal image super-resolution
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -