Simultaneous image fusion and super-resolution using sparse representation

Authors:
Haitao Yin;Shutao Li;Leyuan Fang
Affiliations:
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Venue:
Information Fusion
Year:
2013

Citing 14
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Abstract

Given multiple source images of the same scene, image fusion integrates the inherent complementary information into one single image, and thus provides a more complete and accurate description. However, when the source images are of low-resolution, the resultant fused image can still be of low-quality, hindering further image analysis. To improve the resolution, a separate image super-resolution step can be performed. In this paper, we propose a novel framework for simultaneous image fusion and super-resolution. It is based on the use of sparse representations, and consists of three steps. First, the low-resolution source images are interpolated and decomposed into high- and low-frequency components. Sparse coefficients from these components are then computed and fused by using image fusion rules. Finally, the fused sparse coefficients are used to reconstruct a high-resolution fused image. Experiments on various types of source images (including magnetic resonance images, X-ray computed tomography images, visible images, infrared images, and remote sensing images) demonstrate the superiority of the proposed method both quantitatively and qualitatively.