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Hybrid edge and feature-based single-image superresolution

[+] Author Affiliations
Mohammad Moinul Islam

Old Dominion University, 5115 Hampton Boulevard, Norfolk, Virginia 23529, United States

Mohammed Nazrul Islam

Farmingdale State University of New York, 2350 Broadhollow Road, Farmingdale, New York 11735, United States

Vijayan K. Asari

University of Dayton, 300 College Park, Dayton, Ohio 45469, United States

Mohammad A. Karim

University of Massachusetts Dartmouth, 285 Old Westport Road, Dartmouth, Massachusetts 02747, United States

J. Electron. Imaging. 25(4), 043005 (Jul 12, 2016). doi:10.1117/1.JEI.25.4.043005
History: Received October 20, 2015; Accepted June 20, 2016
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Abstract.  A neighborhood-dependent component feature learning method for regression analysis in single-image superresolution is presented. Given a low-resolution input, the method uses a directional Fourier phase feature component to adaptively learn the regression kernel based on local covariance to estimate the high-resolution image. The unique feature of the proposed method is that it uses image features to learn about the local covariance from geometric similarity between the low-resolution image and its high-resolution counterpart. For each patch in the neighborhood, we estimate four directional variances to adapt the interpolated pixels. This gives us edge information and Fourier phase gives features, which are combined to interpolate using kernel regression. In order to compare quantitatively with other state-of-the-art techniques, root-mean-square error and measure mean-square similarity are computed for the example images, and experimental results show that the proposed algorithm outperforms similar techniques available in the literature, especially at higher resolution scales.

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Citation

Mohammad Moinul Islam ; Mohammed Nazrul Islam ; Vijayan K. Asari and Mohammad A. Karim
"Hybrid edge and feature-based single-image superresolution", J. Electron. Imaging. 25(4), 043005 (Jul 12, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.4.043005


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Single Image Super Resolution using a Joint GMM Method. IEEE Trans Image Process Published online Jul 07, 2016;
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