Infrared surveillance image super resolution via group sparse representation

Infrared (IR) surveillance image generally has low resolution and signal-to-noise ratios. In this paper, we present a new approach to IR image super resolution via group sparse representation (GS). First based on IR image feature, an algorithm of combining the group orthogonal matching pursuit and K...

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Bibliographic Details
Published in:Optics communications 2013-02, Vol.289, p.45-52
Main Authors: Liu, Hai-cang, Li, Shu-tao, Yin, Hai-tao
Format: Article
Language:English
Subjects:
Dictionaries
Dictionary training
Group sparse representation
Infrared
Infrared radiation
Infrared surveillance image
Representations
Signal to noise ratio
Super resolution
Surveillance
Trains
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Summary:Infrared (IR) surveillance image generally has low resolution and signal-to-noise ratios. In this paper, we present a new approach to IR image super resolution via group sparse representation (GS). First based on IR image feature, an algorithm of combining the group orthogonal matching pursuit and K-SVD is proposed to train the dictionaries. The dictionary training can ensure that the corresponding low resolution (LR) and high resolution (HR) image have same GS coefficients. Then the group sparse coefficients of the input LR image are sought to reconstruct the HR image with the trained LR and HR dictionary pair. Experimental results indicate that our method generates sharper results with higher Peak Signal to Noise Ratio (PSNR), and is more robust to noise than several popular methods.
ISSN:0030-4018
1873-0310
DOI:10.1016/j.optcom.2012.10.014