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Journal of Electronic Imaging

Hybrid edge and feature-based single-image superresolution
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Paper 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.

Paper Details

Date Published: 12 July 2016
PDF: 8 pages
J. Electron. Imag. 25(4) 043005 doi: 10.1117/1.JEI.25.4.043005
Published in: Journal of Electronic Imaging Volume 25, Issue 4
Show Author Affiliations
Mohammad Moinul Islam, Old Dominion Univ. (United States)
Mohammed Nazrul Islam, Farmingdale State College (United States)
Vijayan K. Asari, Univ. of Dayton (United States)
Mohammad Ataul Karim, Univ. of Massachusetts Dartmouth (United States)

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