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Journal of Electronic Imaging

Single-image super-resolution based on Markov random field and contourlet transform
Author(s): Wei Wu; Zheng Liu; Wail Gueaieb; Xiaohai He
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Paper Abstract

Learning-based methods are well adopted in image super-resolution. In this paper, we propose a new learning-based approach using contourlet transform and Markov random field. The proposed algorithm employs contourlet transform rather than the conventional wavelet to represent image features and takes into account the correlation between adjacent pixels or image patches through the Markov random field (MRF) model. The input low-resolution (LR) image is decomposed with the contourlet transform and fed to the MRF model together with the contourlet transform coefficients from the low- and high-resolution image pairs in the training set. The unknown high-frequency components/coefficients for the input low-resolution image are inferred by a belief propagation algorithm. Finally, the inverse contourlet transform converts the LR input and the inferred high-frequency coefficients into the super-resolved image. The effectiveness of the proposed method is demonstrated with the experiments on facial, vehicle plate, and real scene images. A better visual quality is achieved in terms of peak signal to noise ratio and the image structural similarity measurement.

Paper Details

Date Published: 1 April 2011
PDF: 18 pages
J. Electron. Imaging. 20(2) 023005 doi: 10.1117/1.3580750
Published in: Journal of Electronic Imaging Volume 20, Issue 2
Show Author Affiliations
Wei Wu, Sichuan Univ. (China)
Zheng Liu, Univ. of Ottawa (Canada)
Wail Gueaieb, Univ. of Ottawa (Canada)
Xiaohai He, Sichuan Univ. (China)

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